Abstract 150: Integrating spatial metabolomics and spatial transcriptomics on the same cancer tissue sections to detect gene-metabolite correlations
Authors: Trevor M. Godfrey, Yasmin Shanneik, Wanqiu Zhang, Thao Tran, Nico Verbeeck, Nathan H. Patterson, Faith E. Jackobs, Maheswhari Ramineni, Chandandeep Nagi, Livia S. Eberlin
Journal: Cancer Research (2025)

Abstract The combination of spatial metabolomics and spatial transcriptomics can reveal powerful connections between gene expression and metabolism in heterogeneous tissues. However, section-to-section variability can convolute data integration. We present a novel approach, termed Desium, which integrates spatial metabolomics by Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) and Visium Spatial Transcriptomics (VST) on the same tissue section. This work demonstrates that DESI-MSI does not significantly impact RNA quality, enabling robust analysis of gene-metabolite relationships in human breast and lung cancer tissues. The Desium workflow involves sequentially performing DESI-MSI at 100 μm spatial resolution, hematoxylin and eosin (H&amp;E) staining, and VST CytAssist analyses on the same tissue section. We applied this workflow to six human cancer tissues (3 breast, 3 lung) and analyzed a serial section of two samples (1 breast, 1 lung) with standard VST CytAssist as a control. H&amp;E and VST data were automatically aligned, while DESI-MSI and H&amp;E data were coregistered manually using nonrigid registration. The MSI data was then converted into VST spot coordinates using a Gaussian granularity matching algorithm. Pearson correlation scores were calculated for each tissue to link gene expression and metabolite abundances. Comparing sequencing results between Desium and the standard VST protocol using UMI counts/gene, we found a correlation coefficient of 1 for both lung and breast samples, indicating minimal impact of DESI-MSI on gene expression measurements. Across all samples, we identified 15, 111 transcript-metabolite correlations from 1, 136 transcripts and 439 metabolites, with correlation scores above 0.5 or below -0.5. Notably, we observed a 0.82 correlation between glycerophosphorylethanolamine and XBP1 in breast cancer and a 0.77 correlation between phosphatidylglycerol PG 22:6_22:5 and MSLN in lung cancer. Our study demonstrates that Desium enables unambiguous spatial correlations between metabolites and RNA transcripts, uncovering novel relationships and unique patterns of intratumor heterogeneity not evident through histologic analysis. These findings suggest that Desium could serve as a powerful tool to explore the gene expression-metabolic phenotype relationships within the complex tumor microenvironment. Citation Format: Trevor M. Godfrey, Yasmin Shanneik, Wanqiu Zhang, Thao Tran, Nico Verbeeck, Nathan H. Patterson, Faith E. Jackobs, Maheswhari Ramineni, Chandandeep Nagi, Livia S. Eberlin. Integrating spatial metabolomics and spatial transcriptomics on the same cancer tissue sections to detect gene-metabolite correlations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 150.