1. Parallel sample processing for mass spectrometry-based single cell proteomics.
- Author
-
Wang J, Xue B, Awoyemi O, Yuliantoro H, Mendis LT, DeVor A, Valentine SJ, and Li P
- Subjects
- Humans, Lab-On-A-Chip Devices, Chromatography, Liquid methods, Peptides analysis, Peptides chemistry, Single-Cell Analysis, Proteomics methods, Mass Spectrometry methods
- Abstract
Background: Single cell mass spectrometry (scMS) has shown great promise for label free proteomics analysis recently. To present single cell samples for proteomics analysis by MS is not a trivial task. Existing methods rely on robotic liquid handlers to scale up sample preparation throughput. The cost associated with specialized equipment hinders the broad adoption of these workflows, and the sequential sample processing nature limits the ultimate throughput., Results: In this work, we report a parallel sample processing workflow that can simultaneously process 10 single cells without the need of robotic liquid handlers for scMS. This method utilized 3D printed microfluidic devices to form reagent arrays on a glass slide, and a magnetic beads-based streamlined sample processing workflow to present peptides for LC-MS detection. We optimized key operational parameters of the method and demonstrated the quantification consistency among 10 parallel processed samples. Finally, the utility of the method in differentiating cell lines and studying the proteome change induced by drug treatment were demonstrated., Significance: The present method allows parallel sample processing for single cells without the need of expensive liquid handlers, which has great potential to further improve throughput and decrease the barrier for single cell proteomics., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Peng Li reports financial support was provided by the National Institutes of Health and the National Science Foundation., (Copyright © 2024 Elsevier B.V. All rights reserved.)
- Published
- 2024
- Full Text
- View/download PDF