Aiding the Exploration of Multiple-Sample Imaging Experiments by Providing Native Data Support for Waters Mass Spectrometry Imaging Data in the SCiLS Lab Software Platform

Importance of the topic

Mass spectrometry imaging offers spatially resolved molecular information critical for understanding tumor heterogeneity and treatment response, particularly in studies of radiotherapy resistance.

Goals and overview of the study

This study demonstrates the integration of multiple DESI-MSI datasets of colorectal tumor xenografts under different irradiation regimens into a single SCiLS Lab project with direct support for Waters native raw files to enable comprehensive comparative analysis.

Methodology and used instrumentation

The workflow involved cryo-sectioning HCT116 xenograft tissues at 12 µm, performing DESI-MS imaging in negative ion mode on a Waters Xevo G2-XS QTof (50–1200 Da, 5 Hz), followed by hematoxylin & eosin and immunofluorescence staining. Data were managed in MassLynx v4.2, imported via Waters HD Imaging v1.6 into SCiLS Lab 2021c on a system with Intel Xeon E5-2690 v4, 128 GB RAM, NVIDIA Quadro K620, Windows 10 Pro.

Main results and discussion

All 18 GB of raw data were imported natively in 1 h 18 min, creating a unified m/z array for multivariate analysis. Co-registration of optical and MS images allowed overlay of selected ions, revealing lipid distributions in viable, hypoxic, and necrotic regions. Principal component analysis of defined viable regions discriminated untreated, mid-treatment, and post-treatment groups and highlighted key lipid species correlated with irradiation.

Benefits and practical applications of the method

Elimination of file conversion optimizes the workflow and ensures consistency across samples. SCiLS Lab provides integrated visualization, statistical tools, and project management, with unique support for 3D data reconstruction, facilitating high-throughput comparative MSI studies.

Future trends and potential applications

Further expansion of native data support to additional instrument platforms, integration of machine learning for automated segmentation, and broader adoption in clinical and pharmaceutical research for detailed spatial omics investigations are anticipated.

Conclusion

Native support for Waters raw MSI data in SCiLS Lab streamlines multi-sample projects, enabling seamless spatial and statistical exploration within a single interface and accelerating insights into treatment-induced biochemical changes.

Reference

No additional literature references were provided in the source document.