时序性的肺腺癌小鼠类器官模型临界状态的蛋白质组数据集

时序性的类器官模型因其在肿瘤细胞精准建模、机制研究、药物研发等方面的优势和应用前景，有着特殊重要地位。基于类器官模型，我们采集蛋白质组数据依赖（DDA）数据建库规模已达到了6400种蛋白质，数据非依赖（DIA）数据可以重现90%数据库中的蛋白质定性与定量。接下来，我们运用这一新流程，对时序性(P0-P9共10个代次)的肺腺癌小鼠类器官模型临界状态的蛋白质组数据集进行采集与数据分析。P0-P9的10个代次每个代次3个类器官组织，共30个样本采用SP3蛋白质样品制备的方法，并采取DIA数据采集分析模式，共鉴定到4659种蛋白质。筛选显著差异表达蛋白质（1383个）进行聚类分析，结果显示P4-P6代可能是肺腺鳞癌转分化的关键时期。对显著差异表达的蛋白质做生物学途径富集分析，结果显示：脂肪酸代谢、半乳糖代谢、糖酵解/糖异生等重要的生物学代谢途径在P4-P6代有波动下调与波动上调的趋势，提示P4-P6代可能是肺腺鳞癌转分化的关键时期。

Time-series organoid models hold a particularly important position due to their advantages and application prospects in precise tumor cell modeling, mechanism research, drug development and other fields. Based on the organoid model, the database constructed using Data-Dependent Acquisition (DDA) proteomic data has reached a scale of 6400 proteins, and Data-Independent Acquisition (DIA) data can reproduce 90% of the protein identification and quantification results in the database. Next, we applied this new workflow to collect and analyze the proteomic dataset of the critical states of mouse lung adenocarcinoma organoid models with 10 passages (P0-P9 in total). For the 10 passages from P0 to P9, 3 organoid tissues were collected for each passage, totaling 30 samples. We used the SP3 protein sample preparation method and adopted the DIA data acquisition and analysis mode, and identified a total of 4659 proteins. We screened 1383 significantly differentially expressed proteins for cluster analysis, and the results showed that passages P4-P6 may be the critical period for transdifferentiation of lung adenosquamous carcinoma. Functional enrichment analysis of biological pathways was performed on the significantly differentially expressed proteins, and the results showed that important biological metabolic pathways such as fatty acid metabolism, galactose metabolism, glycolysis/gluconeogenesis exhibited fluctuating down-regulation and up-regulation trends at passages P4-P6, suggesting that P4-P6 may be the critical period for lung adenosquamous carcinoma transdifferentiation.