DIA-enabled discovery of alternative proteins (AltProts) in mouse heart development

In this project, we conducted an evaluation of mass spectrometry methods using mouse heart and HCT116 cell samples. The results demonstrated that the data-independent acquisition (DIA) approach outperformed data-dependent acquisition (DDA) in the identification of Altprot and canonical proteins. Subsequently, we assessed several different DIA library building methods, including traditional DDA-based library building, gas-phase fractionation(GPF) library building, and machine learning-based prediction library building. Notably, the traditional DDA library building method exhibited a higher likelihood of false positive identifications. Furthermore, we applied the aforementioned mass spectrometry methods to investigate the process of mouse heart development. Through this analysis, we identified a subset of Altprots, including ASDURF, which may potentially play crucial roles in heart development. These findings serve as a fundamental basis for future exploration and investigation of Altprot in this context.本研究采用小鼠心脏与HCT116细胞样本对质谱法开展了系统性评估。结果表明，在可变蛋白（Altprot）与经典蛋白（canonical proteins）的鉴定工作中，数据非依赖采集（data-independent acquisition，DIA）策略的表现优于数据依赖采集（data-dependent acquisition，DDA）。随后，我们对多种不同的DIA文库构建方法进行了评测，包括传统基于DDA的文库构建、气相分级（gas-phase fractionation，GPF）文库构建以及基于机器学习的预测文库构建。值得注意的是，传统DDA文库构建方法出现假阳性鉴定结果的概率更高。此外，我们将上述质谱分析方法应用于小鼠心脏发育过程的研究。通过本次分析，我们鉴定出了一类包含ASDURF在内的可变蛋白子集，这类蛋白可能在心脏发育过程中发挥关键作用。本研究结果为后续针对该场景下可变蛋白的探索与研究奠定了基础。