Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry

• Published in: Journal of proteome research 2023-02, Vol.22 (2), p.491-500
• Main Authors: Xie, Yuxuan Richard, Chari, Varsha K., Castro, Daniel C., Grant, Romans, Rubakhin, Stanislav S., Sweedler, Jonathan V.
• Format: Article
• Language: English
• Subjects: Brain; Machine Learning; Mass Spectrometry - methods; Single-Cell Analysis - methods; Workflow
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.2c00714

Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.