13C Qualitative Flux Analysis with Symphony Software and Polly Software of Non-Small Cell Lung Carcinoma Cells Grown in vitro in Two and Three Dimensions

Significance of the Topic

Understanding metabolic fluxes in cancer cells is critical for revealing how tumor microenvironments influence nutrient utilization and therapy response. Traditional two-dimensional (2D) monolayer cultures often fail to reproduce the complexity of in vivo tumors. Three-dimensional (3D) spheroid models better mimic cell–cell and cell–matrix interactions, leading to differences in metabolic pathways. Comparing flux through glycolysis and the tricarboxylic acid (TCA) cycle in 2D versus 3D cultures can guide more predictive preclinical studies.

Objectives and Study Overview

This case study aimed to apply a fully automated informatics pipeline using Symphony and Polly™ software to qualitatively compare glucose-derived fluxes in H1299 non-small cell lung carcinoma cells grown as 2D monolayers and 3D spheroids. Cells were labeled with uniformly 13C-enriched glucose to trace metabolite enrichment patterns. The goals were:

• Implement an end-to-end workflow from raw LC-MS data to pathway mapping.
• Contrast isotopologue distributions in key metabolites between culture geometries.
• Demonstrate the impact of 3D growth on glycolytic and TCA cycle activity.

Methodology and Instrumentation

Cell Culture and Extraction:

• H1299 cells grown in monolayer on standard tissue culture plates or as spheroids in pHEMA-coated dishes.
• Labeling with [U-13C6]-glucose in culture medium.
• Cell lysis by heat shock, liquid–liquid extraction (MeOH/CHCl3), protein precipitation, and sample drying.
• Reconstitution in appropriate solvent and injection into LC-MS.

LC-MS Conditions:

• Waters ACQUITY UPLC I-Class system with BEH C18 column (1.7 μm, 2.1 × 50 mm) at 40 °C.
• Mobile phases A (0.1% formic acid in water), B (0.1% formic acid in acetonitrile), and D (0.1% formic acid in IPA/ACN 90/10).
• Gradient elution: 5% B to 98% B over 8 min; 2 min hold; return to initial conditions.
• Xevo G2-XS QTof in positive ESI mode, MS E acquisition, 50–1,200 m/z, 30,000 FWHM resolution.

Data Processing Pipeline:

• Automated triggering by MassLynx and Symphony client/server.
• Conversion of raw files to mzXML via MSConvert.
• Peak detection and integration in ElMaven.
• Isotopologue correction and pathway visualization in PollyPhi.

Main Results and Discussion

Glycolysis Upregulation:
The 13C3-lactate isotopologue fraction was significantly higher in 3D spheroids, indicating elevated glycolytic flux compared to 2D monolayers.

Enhanced TCA Cycle Input:
Key TCA intermediates showed increased 13C2 labeling in spheroids, implying greater conversion of pyruvate to acetyl-CoA by pyruvate dehydrogenase. This aligns with the boosted glycolytic output and suggests a coordinated rise in mitochondrial metabolism in 3D cultures.

These observations highlight how 3D architecture alters central carbon metabolism and may affect drug sensitivity and metabolic targeting strategies.

Benefits and Practical Applications

• Automated, reproducible workflow reduces manual curation and accelerates metabolic flux studies.
• Integration of Symphony and Polly supports high-throughput comparative analysis of culture models.
• Insights into 3D-specific metabolic adaptations can inform more physiologically relevant drug screening and biomarker discovery.

Future Trends and Potential Applications

Advances may include:

• High-resolution fluxomics at the single-cell level to capture intra-spheroid heterogeneity.
• Integration with machine-learning models for predictive metabolic phenotyping.
• Expansion to multi-omics platforms, combining fluxomics with proteomics and transcriptomics in 3D tumor models.
• Clinical translation through organoid-based metabolite flux profiling for personalized therapy design.

Conclusion

This study demonstrates that 3D spheroid culture markedly increases glucose flux through glycolysis and the TCA cycle compared to 2D monolayers. The automated Symphony–Polly pipeline enables streamlined qualitative flux analysis, underlining the importance of model geometry in metabolic research and its implications for in vivo translation.

Used Instrumentation

• ACQUITY UPLC I-Class System
• Xevo G2-XS QTof Mass Spectrometer
• MassLynx Software
• Symphony Software with MSConvert, ElMaven, and PollyPhi Modules

Reference

1. Li et al. Survival advantages of multicellular spheroids vs. monolayers of HepG2 cells in vitro. Oncol Rep. 2008;20(6):1465–71.
2. Wallace et al. A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ2. Comput Math Methods Med. 2016;2016:3628124.
3. Fernandez et al. The mitochondrial citrate carrier, SLC25A1, drives stemness and therapy resistance in non-small cell lung cancer. Cell Death Differ. 2018;25(7):1239–58.
4. Automating Metabolic Flux Analysis with Symphony and Polly. Waters Corporation Technical Brief 720006380EN. 2018.