Single Cell Mass Spectrometry With a Robotic Micromanipulation System for Cell Metabolite Analysis

Objective: The increasing demand for unraveling cellular heterogeneity has boosted single cell metabolomics studies. However, current analytical methods are usually labor-intensive and hampered by lack of accuracy and efficiency. Methods: we developed a first-ever automated single cell mass spectrometry system (named SCMS) that facilitated the metabolic profiling of single cells. In particular, extremely small droplets of sub nano-liter were generated to extract the single cells, and the underlying mechanism was verified theoretically and experimentally. This was crucial to minimize the dilution of the trace cellular contents and enhance the analytical sensitivity. Based on the precise 3D positioning of the pipette tip, we established a visual servoing robotic micromanipulation platform on which single cells were sequentially extracted, aspirated, and ionized, followed by the mass spectrometry analyses. Results: With the SCMS system, inter-operator variability was eliminated and working efficiency was improved. The performance of the SCMS system was validated by the experiments on bladder cancer cells. MS and MS2 analyses of single cells enable us to identify several cellular metabolites and the underlying inter-cell heterogeneity. Conclusion: In contrast to traditional methods, the SCMS system functions without human intervention realizes a robust single cell metabolic analysis. Significance: the SCMS system upgrades the way how single cell metabolites were analyzed, and has the potential to be a powerful tool for single cell metabolomics studies.

Automated summary

The increasing demand for unraveling cellular heterogeneity has led to the development of single cell metabolomics studies. However, current analytical methods are labor-intensive and lack accuracy and efficiency. In this study, an automated single cell mass spectrometry system (SCMS) was developed to improve the metabolic profiling of single cells by eliminating operator variability and increasing working efficiency. The performance of the system was validated through experiments on bladder cancer cells, highlighting its potential as a powerful tool for single cell metabolomics studies.