Automated and Miniaturized Pico-Liter Metabolite Extraction System for Single-Cell Mass Spectrometry

Objective: Mass spectrometry has become the method of choice for single cell analysis due to its high sensitivity of detection and capability in analyzing a large number of metabolites simultaneously. For a long time, an automated and miniaturized system capable of extracting cellular contents from single cells at the pico-liter level for pico-ESI analysis has been lacking. Methods: This paper presents a first-of-its-kind automated and miniaturized pico-liter extraction system for single-cell MS. The key modules, including imaging, bus controller, and fluidic driving are customized to achieve satisfactory performance at affordable costs, resulting in a miniaturized system movable on a trolley and connectable with the MS. To enable automation, a single cell trapping device, new image-based one-pixel accuracy positioning methods for cells and micropipette, and a surface-tension-based 1-pL accuracy volume control scheme are developed. Results: The system is able to control the solvent loading at 1.97 +/- 0.05 nL, solvent dispensing at 14-15 pL, and solvent evaporation at 689 +/- 48 pL. MS experiments demonstrate a throughput of 20 cells/h. Conclusion: The system has achieved better performance in consistency (similar to 21%), sensitivity (similar to 28%), and success rate (up to 40%) than manual operation. Significance: This automated and miniaturized system lays a solid basis for applying pico-ESI MS analysis in the automated and high-throughput single cell MS analysis, such as single-cell metabolomics and lipidomics.

Automated summary

This study developed an automated and miniaturized picoliter extraction system for single-cell mass spectrometry analysis, which achieved better performance in consistency, sensitivity, and success rate compared to manual operation. The system lays a solid basis for applying picoliter electrospray ionization MS analysis in automated and high-throughput single cell metabolomics and lipidomics.