Huang Ting, Wang Jian, Stukalov Alexey, Donovan Margaret K R, Ferdosi Shadi, Williamson Lucy, Just Seth, Castro Gabriel, Cantrell Lee S, Elgierari Eltaher, Benz Ryan W, Huang Yingxiang, Motamedchaboki Khatereh, Hakimi Amirmansoor, Arrey Tabiwang, Damoc Eugen, Kreimer Simion, Farokhzad Omid C, Batzoglou Serafim, Siddiqui Asim, Van Eyk Jennifer E, Hornburg Daniel
Seer, Inc., Redwood City, CA, 94065 USA.
Thermo Fisher Scientific, San Jose, CA, USA.
bioRxiv. 2023 Aug 29:2023.08.28.555225. doi: 10.1101/2023.08.28.555225.
The wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics.
Investigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments.
We show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts.
The automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.
循环蛋白的动态范围广泛,加上血浆中存在的蛋白异构体的多样性,长期以来阻碍了对血浆蛋白质组进行大规模的全面定量表征。基于自动纳米颗粒(NP)蛋白冠的蛋白质组学工作流程可以有效地将蛋白质丰度的动态范围压缩到质谱(MS)可检测的范围内。这提高了基于定量MS方法的深度和可扩展性,这些方法可以阐明生物过程的分子机制,发现新的蛋白质生物标志物,并提高基于MS诊断的全面性。
通过研究多物种掺入实验和一个队列,我们结合多种MS仪器,研究了使用深度血浆蛋白质组学工作流程Proteograph产品套件的倍数变化准确性、线性、精密度和统计功效。
我们表明,基于NP的工作流程能够从血浆中准确鉴定(错误发现率为1%)超过6000种蛋白质(Orbitrap Astral),并且与仅限于检测数百种血浆蛋白质的金标准纯血浆工作流程相比,能够以准确的倍数变化、高线性和精密度对更多蛋白质进行定量。此外,我们展示了在小规模和大规模队列中发现生物标志物的高统计功效。
自动化NP工作流程能够进行高通量、深度和定量的血浆蛋白质组学研究,有足够的能力以肽水平分辨率发现新的生物标志物特征。