SMAnalyst: A Web Server for Spatial Metabolomic Data Analysis and Annotation

TL;DR

SMAnalyst is a new web-based tool that simplifies the analysis of spatial metabolomic data, offering a complete workflow from quality control to differential analysis.

Contribution

SMAnalyst introduces an integrated, open-source GUI platform for spatial metabolomics analysis, combining multiple functionalities in a single tool.

Findings

SMAnalyst consolidates quality control, annotation, and spatial pattern discovery in a single platform.

The tool efficiently processes large datasets, such as over 14,000 pixels and 3,000 ion peaks.

SMAnalyst reduces the need for advanced computational skills and tool switching in spatial metabolomics.

Abstract

Spatial metabolomics is a rapidly advancing field offering powerful insights into metabolic heterogeneity in biological tissues. However, its widespread adoption is hindered by fragmented tools and the lack of comprehensive, open-source GUI software covering the full analytical workflow (quality control, preprocessing, identification, pattern, and differential analysis). To address this, we developed SMAnalyst, an open-source, integrated web-based platform. SMAnalyst consolidates core functionalities, including multi-dimensional data quality assessment (background consistency, intensity, missing values), a comprehensive metabolite annotation scoring system (mass accuracy, isotopic similarity, adduct evidence), and dual-dimension spatial pattern discovery (metabolite co-expression and pixel clustering). It also offers flexible differential analysis (cluster- or user-defined regions). With its intuitive GUI and modular workflow, SMAnalyst significantly lowers the analysis barrier, by providing a unified solution that eliminates the need for tool switching and advanced computational skills. Tested with a mouse brain dataset, SMAnalyst efficiently handles large-scale data (e.g., >14,000 pixels, >3000 ion peaks), effectively filling a critical gap in integrated analytical solutions for spatial metabolomics.

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