five

Simple and Integrated Spintip-Based Technology Applied for Deep Proteome Profiling

收藏
NIAID Data Ecosystem2026-03-09 收录
下载链接:
https://figshare.com/articles/dataset/Simple_and_Integrated_Spintip_Based_Technology_Applied_for_Deep_Proteome_Profiling/3187381
下载链接
链接失效反馈
官方服务:
资源简介:
Great efforts have been taken for developing high-sensitive mass spectrometry (MS)-based proteomic technologies, among which sample preparation is one of the major focus. Here, a simple and integrated spintip-based proteomics technology (SISPROT) consisting of strong cation exchange beads and C18 disk in one pipet tip was developed. Both proteomics sample preparation steps, including protein preconcentration, reduction, alkylation, and digestion, and reversed phase (RP)-based desalting and high-pH RP-based peptide fractionation can be achieved in a fully integrated manner for the first time. This easy-to-use technology achieved high sensitivity with negligible sample loss. Proteomic analysis of 2000 HEK 293 cells readily identified 1270 proteins within 1.4 h of MS time, while 7826 proteins were identified when 100000 cells were processed and analyzed within only 22 h of MS time. More importantly, the SISPROT can be easily multiplexed on a standard centrifuge with good reproducibility (Pearson correlation coefficient > 0.98) for both single-shot analysis and deep proteome profiling with five-step high-pH RP fractionation. The SISPROT was exemplified by the triplicate analysis of 100000 stem cells from human exfoliated deciduous teeth (SHED). This led to the identification of 9078 proteins containing 3771 annotated membrane proteins, which was the largest proteome data set for dental stem cells reported to date. We expect that the SISPROT will be well suited for deep proteome profiling for fewer than 100000 cells and applied for translational studies where multiplexed technology with good label-free quantification precision is required.
创建时间:
2016-04-27
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作