Ghosh Gautam, Shannon Ariana E, Searle Brian C
Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio, USA.
Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA.
Proteomics. 2025 Jan;25(1-2):e2400022. doi: 10.1002/pmic.202400022. Epub 2024 Aug 1.
Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.
单细胞蛋白质组学（SCP）旨在表征单个细胞的蛋白质组，为复杂生物系统提供深入见解。它揭示了整体蛋白质组分析可能忽略的不同细胞群体之间的细微差异，这对于理解疾病机制和开发靶向治疗至关重要。SCP中的质谱（MS）方法能够识别和定量单个细胞中的数千种蛋白质。SCP面临的两个主要挑战是单细胞样本中的材料有限，需要高度灵敏的分析技术，以及样本的高效处理，因为每个生物样本都需要进行数千次单细胞测量。本综述讨论了使用数据依赖型采集（DDA）和数据非依赖型采集（DIA）来缓解这些挑战的质谱技术进展。此外，我们还研究了使用短液相色谱梯度和样本多路复用方法，这些方法可提高SCP实验的样本通量和可扩展性。我们相信这些方法将为增进我们对细胞异质性及其对系统生物学的影响的理解铺平道路。
