蛋白质组
蛋白质组学
工作流程
计算生物学
吞吐量
定量蛋白质组学
质谱法
样品制备
单细胞分析
样品(材料)
计算机科学
化学
细胞
色谱法
生物信息学
生物
生物化学
基因
数据库
无线
电信
作者
Claudia Ctortecka,Natalie M. Clark,Brian Boyle,Anjali Seth,D.R. Mani,Namrata D. Udeshi,Steven A. Carr
标识
DOI:10.1101/2024.01.20.576369
摘要
Abstract Mass spectrometry (MS)-based single-cell proteomics (SCP) has gained massive attention as a viable complement to other single cell approaches. The rapid technological and computational advances in the field have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell at reasonable proteome depth to characterize biological phenomena remains a challenge. To address some of those limitations we present a combination of fully automated single cell sample preparation utilizing a dedicated chip within the picolitre dispensing robot, the cellenONE. The proteoCHIP EVO 96 can be directly interfaced with the Evosep One chromatographic system for in-line desalting and highly reproducible separation with a throughput of 80 samples per day. This, in combination with the Bruker timsTOF MS instruments, demonstrates double the identifications without manual sample handling. Moreover, relative to standard high-performance liquid chromatography, the Evosep One separation provides further 2-fold improvement in protein identifications. The implementation of the newest generation timsTOF Ultra with our proteoCHIP EVO 96-based sample preparation workflow reproducibly identifies up to 4,000 proteins per single HEK-293T without a carrier or match-between runs. Our current SCP depth spans over 4 orders of magnitude and identifies over 50 biologically relevant ubiquitin ligases. We complement our highly reproducible single-cell proteomics workflow to profile hundreds of lipopolysaccharide (LPS)-perturbed THP-1 cells and identified key regulatory proteins involved in interleukin and interferon signaling. This study demonstrates that the proteoCHIP EVO 96-based SCP sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.
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