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微柱阵列、宽视窗采集和基于人工智能的数据分析可提高多种蛋白质组学应用的全面性。

Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications.

机构信息

Research Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria.

Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.

出版信息

Nat Commun. 2024 Feb 3;15(1):1019. doi: 10.1038/s41467-024-45391-z.

Abstract

Comprehensive proteomic analysis is essential to elucidate molecular pathways and protein functions. Despite tremendous progress in proteomics, current studies still suffer from limited proteomic coverage and dynamic range. Here, we utilize micropillar array columns (µPACs) together with wide-window acquisition and the AI-based CHIMERYS search engine to achieve excellent proteomic comprehensiveness for bulk proteomics, affinity purification mass spectrometry and single cell proteomics. Our data show that µPACs identify ≤50% more peptides and ≤24% more proteins, while offering improved throughput, which is critical for large (clinical) proteomics studies. Combining wide precursor isolation widths of m/z 4-12 with the CHIMERYS search engine identified +51-74% and +59-150% more proteins and peptides, respectively, for single cell, co-immunoprecipitation, and multi-species samples over a conventional workflow at well-controlled false discovery rates. The workflow further offers excellent precision, with CVs <7% for low input bulk samples, and accuracy, with deviations <10% from expected fold changes for regular abundance two-proteome mixes. Compared to a conventional workflow, our entire optimized platform discovered 92% more potential interactors in a protein-protein interaction study on the chromatin remodeler Smarca5/Snf2h. These include previously described Smarca5 binding partners and undescribed ones including Arid1a, another chromatin remodeler with key roles in neurodevelopmental and malignant disorders.

摘要

综合蛋白质组学分析对于阐明分子途径和蛋白质功能至关重要。尽管蛋白质组学取得了巨大进展,但目前的研究仍然受到有限的蛋白质组覆盖范围和动态范围的限制。在这里,我们利用微柱列(µPACs)结合宽窗口采集和基于人工智能的 CHIMERYS 搜索引擎,实现了批量蛋白质组学、亲和纯化质谱和单细胞蛋白质组学的出色蛋白质组综合分析能力。我们的数据表明,µPACs 可以鉴定出≤50%更多的肽段和≤24%更多的蛋白质,同时提供了更高的通量,这对于大型(临床)蛋白质组学研究至关重要。结合 m/z 4-12 的宽前体分离宽度和 CHIMERYS 搜索引擎,与传统工作流程相比,单细胞、共免疫沉淀和多物种样本分别鉴定出+51-74%和+59-150%更多的肽段和蛋白质,在很好地控制假发现率的情况下。该工作流程还提供了出色的精度,低输入批量样本的 CVs<7%,以及对于常规丰度两蛋白混合物的预期倍数变化的偏差<10%的准确性。与传统工作流程相比,在染色质重塑因子 Smarca5/Snf2h 的蛋白质-蛋白质相互作用研究中,我们整个优化的平台发现了 92%更多的潜在相互作用蛋白。其中包括之前描述的 Smarca5 结合伙伴,以及之前未描述的 Arid1a,另一种染色质重塑因子,在神经发育和恶性疾病中具有关键作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6249/10838342/16d6425157d5/41467_2024_45391_Fig1_HTML.jpg

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