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

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 spectrom-etry 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.

Automated summary

Improved throughput and enhanced detection limits enable researchers to characterize the functional states of individual cells using single-cell chemical analysis. Image-guided single-cell mass spectrometry combines optical microscopy and fluorescence microscopy to analyze cellular and subcellular targets. In this study, a data-driven and machine learning workflow called DATSIGMA was proposed for feature extraction and interpretability of complex single-cell mass spectrometry data. The toolset was successfully implemented and tested on various experimental data sets, showing its potential for diverse biological applications.