TOFSIMS-P: a web-based platform for analysis of large-scale TOF-SIMS data

Area of Science:

• Analytical Chemistry
• Computational Biology
• Oncology

Background:

• Time-of-flight secondary ion mass spectrometry (TOF-SIMS) offers high molecular specificity and spatial resolution for near-surface analysis.
• Analyzing large TOF-SIMS datasets has been challenging due to the lack of automated data processing tools.

Purpose of the Study:

• To develop and validate a computational platform for automated TOF-SIMS data analysis.
• To identify discriminatory ions and metabolic pathways associated with gastric cancer.

Main Methods:

• Development of a computational platform for peak identification, alignment, and classifier building.
• Analysis of 43 TOF-SIMS datasets from gastric cancer and normal tissues.
• Application of partial least square-discriminant analysis (PLS-DA) and network analysis.

Main Results:

• The platform successfully processed 87,138 ions, selecting and aligning 1286 discriminatory ions.
• A PLS-DA model achieved a low misclassification error rate of 0.024 for distinguishing tissue types.
• Network analysis revealed dysregulation in amino acid metabolism in gastric cancer tissues.

Conclusions:

• The proposed computational framework effectively analyzes large-scale TOF-SIMS data for complex biological samples.
• The platform enables accurate discrimination of gastric cancer from normal tissues and identifies potential metabolic biomarkers.