Efficient Sample Preparation System for Multi-Omics Analysis via Single Cell Mass Spectrometry.
Peng ZhaoYongxiang FengJunhan WuJunwen ZhuJinlei YangXiaoxiao MaZheng OuyangXinrong ZhangWenpeng ZhangWenhui WangPublished in: Analytical chemistry (2023)
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 MS 2 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.
Keyphrases
- single cell
- rna seq
- high throughput
- mass spectrometry
- highly efficient
- induced apoptosis
- ms ms
- cell cycle arrest
- multiple sclerosis
- deep learning
- endoplasmic reticulum stress
- high resolution
- squamous cell carcinoma
- oxidative stress
- mental health
- high performance liquid chromatography
- circulating tumor cells
- stem cells
- mesenchymal stem cells
- bone marrow
- cell proliferation
- gas chromatography
- healthcare
- childhood cancer
- data analysis
- signaling pathway
- heavy metals
- young adults