Efficient Sample Preparation System for Multi-Omics Analysis via Single Cell Mass Spectrometry

Mass spectrometry (MS) has become a powerful tool for metabolome, lipidome, and proteome analyses. The efficient analysis of multi-omics in single cells, however, is still challenging in the manipulation of single cells and lack of in-fly cellular digestion and extraction approaches. Here, we present a streamlined strategy for highly efficient and automatic single-cell multi-omics analysis by MS. We developed a 10-pL-level microwell chip for housing individual single cells, whose proteins were found to be digested in 5 min, which is 144 times shorter than traditional bulk digestion. Besides, an automated picoliter extraction system was developed for sampling of metabolites, phospholipids, and proteins in tandem from the same single cell. Also, 2 min MS2 spectra were obtained from 700 pL solution of a single cell sample. In addition, 1391 proteins, phospholipids, and metabolites were detected from one single cell within 10 min. We further analyzed cells digested from cancer tissue samples, achieving up to 40% increase in cell classification accuracy using multi-omics analysis in comparison with single-omics analysis. This automated single-cell MS strategy is highly efficient in analyzing multi-omics information for investigation of cell heterogeneity and phenotyping for biomedical applications.

Automated summary

Mass spectrometry (MS) has become a powerful tool for analyzing metabolome, lipidome, and proteome. However, analyzing multi-omics in single cells is still challenging. In this study, a streamlined strategy for efficient and automatic single-cell multi-omics analysis by MS was developed. The strategy includes the use of a microwell chip for housing single cells, an automated system for extracting metabolites, phospholipids, and proteins, and obtaining MS2 spectra from a single cell sample. The strategy was successfully applied to analyze cancer tissue samples and improved cell classification accuracy compared to single-omics analysis.