Recent methodological advances towards single-cell proteomics.
Sooyeon KimLatiefa KamarulzamanYuichi TaniguchiPublished in: Proceedings of the Japan Academy. Series B, Physical and biological sciences (2023)
Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and we provide an outlook on this research field.
Keyphrases
- single cell
- rna seq
- mass spectrometry
- single molecule
- high throughput
- artificial intelligence
- label free
- induced apoptosis
- machine learning
- deep learning
- cell cycle arrest
- high resolution
- big data
- working memory
- oxidative stress
- signaling pathway
- stem cells
- living cells
- gene expression
- gas chromatography
- copy number
- ms ms
- fluorescent probe