Norm-SVR for the Enhancement of Single-Cell Metabolomic Stability in ToF-SIMS

TL;DR

This paper introduces a new method to improve data stability in single-cell metabolomic analysis using ToF-SIMS.

Contribution

The paper introduces Norm-SVR as a novel correction method for ToF-SIMS single-cell metabolomics.

Findings

Norm-SVR reduces batch effects and variability in ToF-SIMS data.

The method outperforms traditional total ion intensity normalization.

It enhances data quality for large-scale ToF-SIMS analysis.

Abstract

Purpose: Data stability is a critical factor in Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) single-cell analysis. However, various factors, such as sample processing, instrument condition, and data acquisition, can introduce uncertainties into ToF-SIMS data. Correcting this data is vital, yet current methods mainly focus on total ion intensity normalization or using consistent substrates. No specific correction method exists for ToF-SIMS single-cell metabolomics. Methods: This study utilizes the Normalized Support Vector Regression (Norm-SVR), commonly used methods for correcting large-scale metabolomics data, for the correction of ToF-SIMS single-cell metabolomic analysis and assesses its performance in comparison to traditional total ion intensity normalization. Results and Conclusions: The results suggest that Norm-SVR effectively diminishes batch effects and reduces variability, thereby underscoring the method’s efficacy and practicality. This approach is expected to improve data quality assurance in extensive ToF-SIMS analytical datasets.

Figures