Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry
journal contributionposted on 2023-01-25, 13:51 authored by Yuxuan Richard Xie, Varsha K. Chari, Daniel C. Castro, Romans Grant, Stanislav S. Rubakhin, Jonathan V. Sweedler
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.
Read the peer-reviewed publication
ss spectrometry ),mage -< bcell chemical analysiscell analysis usingg </ bda </ bmachine learning approaches</ bmachine learningthroughput analysisworkflow basedwide rangesubcellular targetslowered limitslimited samplesindividual cellsgo beyondfriendly programsfluorescence microscopyfeature extractionenhanced interpretabilityenabling researcherseasily adaptedbiological applicationsbased framework