奥地利分子病理研究所Manuel Matzinger等研究人员合作，在前所未有的准确度下量化单个细胞中的多达5300种蛋白质并揭示细胞异质性。这一研究成果于2025年1月16日在线发表在国际学术期刊《自然—方法学》上。

研究人员展示了在蛋白质组覆盖深度、定量准确性和精度方面的显著提升，能够定量超低输入量。通过定制的库，研究人员从仅250 pg的HeLa细胞肽段中识别出最多7400个蛋白质，且日处理量可达50个样本。使用双蛋白质组混合物，研究人员检查了定量的最佳参数，并显示在单细胞水平输入下，2倍的折叠变化差异仍能成功确定。

最终，研究人员将该工作流程应用于A549细胞，根据细胞大小和使用的搜索策略，得到从单个细胞中提取的蛋白质组覆盖范围，从1801个到超过5300个蛋白质组不等，这使研究人员能够研究细胞大小与细胞周期阶段之间的依赖关系。此外，该工作流程使研究人员能够区分两种人类囊胚谱系的体外类比：初始人类多能干细胞（外胚层）和类滋养层细胞。这些数据与转录组数据和谐对齐，表明单细胞蛋白质组学具有识别囊胚内生物学相关差异的能力。

研究人员表示，尽管在样本准备、仪器设备和数据分析方面取得了重大进展，但单细胞蛋白质组学目前仍受到蛋白质组深度和定量性能的限制。

附：英文原文

Title: Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity

Author: Bubis, Julia A., Arrey, Tabiwang N., Damoc, Eugen, Delanghe, Bernard, Slovakova, Jana, Sommer, Theresa M., Kagawa, Harunobu, Pichler, Peter, Rivron, Nicolas, Mechtler, Karl, Matzinger, Manuel

Issue&Volume: 2025-01-16

Abstract: Despite significant advancements in sample preparation, instrumentation and data analysis, single-cell proteomics is currently limited by proteomic depth and quantitative performance. Here we demonstrate highly improved depth of proteome coverage as well as accuracy and precision for quantification of ultra-low input amounts. Using a tailored library, we identify up to 7,400 protein groups from as little as 250pg of HeLa cell peptides at a throughput of 50 samples per day. Using a two-proteome mix, we check for optimal parameters of quantification and show that fold change differences of 2 can still be successfully determined at single-cell-level inputs. Eventually, we apply our workflow to A549 cells, yielding a proteome coverage ranging from 1,801 to a maximum of >5,300 protein groups from a single cell depending on cell size and search strategy used, which allows for the study of dependencies between cell size and cell cycle phase. Additionally, our workflow enables us to distinguish between in vitro analogs of two human blastocyst lineages: naive human pluripotent stem cells (epiblast) and trophectoderm-like cells. Our data harmoniously align with transcriptomic data, indicating that single-cell proteomics possesses the capability to identify biologically relevant differences within the blastocyst.

DOI: 10.1038/s41592-024-02559-1

Source: https://www.nature.com/articles/s41592-024-02559-1