单细胞蛋白质组学旨在以无偏向方式揭示生物功能和异质性，以蛋白质水平进行表征。目前，其在蛋白质组学深度、通量和鲁棒性方面存在一定局限性。我们通过采用流线型多重激发数据无关采集（mDIA）的工作流程来解决这些问题。我们演示了在不损失蛋白质组学深度的情况下，对大样本或单细胞样本进行自动化和完整的二甲基标记。Lys-N消化能够实现MS1和MS2水平的五重定量。由于多重激发通道之间具有定量隔离性，mDIA可以容纳一个不干扰目标通道的参考通道。我们的算法RefQuant利用了这一点，并且与我们以前的研究相比（Brunner et al，PMID 35226415），对于每个单个细胞可以自信地定量两倍于以前的蛋白质数量。同时，我们的工作流程目前每天允许常规分析80个单细胞。最后，我们将mDIA与空间蛋白质组学相结合，将Deep Visual Proteomics在显微切割和质谱分析方面的通量提高了7倍和4倍。将其应用于原发性切除性黑素瘤时，我们发现细胞在不同肿瘤微环境中的蛋白质标志物，展示了它在精准肿瘤学中的潜力。© 2023 作者。在CC BY 4.0许可协议的条款下发表。

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.© 2023 The Authors. Published under the terms of the CC BY 4.0 license.