Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification.
Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification.
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使用细胞内蛋白酶消化进行的低细胞数蛋白质组学分析揭示了细胞周期状态分类的稳健特征。
DOI：
10.1016/j.mcpro.2021.100169
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Ly T
中科院分区：
文献类型：
--
作者：
Kelly V;Al-Rawi A;Lewis D;Kustatscher G;Ly T
罕见细胞表型的综合蛋白质组分析仍然是一个重大挑战。我们报告了一种低细胞数量基于ms的蛋白质组学方法，使用蛋白酶消化细胞中轻度甲醛固定的细胞，我们称之为“细胞内消化”。我们将其与平均MS1前体文库匹配相结合，定量表征来自低细胞数量的人淋巴母细胞的蛋白质组。从2000个细胞中检测到约4500种蛋白，从200个淋巴细胞中定量检测到2500种蛋白。样品处理的便便性和高灵敏度使得该方法特别适合于罕见细胞状态的蛋白质组学分析，包括免疫细胞亚群和细胞周期亚期。为了证明该方法，我们从异步TK6细胞中分离出16个细胞周期状态（CCSs），表征了蛋白质组的变化，避免了同步。状态包括有丝分裂晚期细胞，出现频率极低。我们鉴定了119种假周期蛋白，它们在细胞周期中变化。伪周期蛋白的聚类显示了与S晚期、G2&M边界、有丝分裂中期和有丝分裂结束时蛋白质降解“波”一致的丰度模式。通过预测核定位和与后期促进复合体/环小体的相互作用的显著差异来区分这些簇。该数据集还确定了有丝分裂中假定的后期促进复合物/环小体底物及其降解目标的时间顺序。我们证明，由这119个高可信度的细胞周期调节蛋白组成的蛋白质特征可以用于对蛋白质组进行无偏分类。我们将该特征应用于296个蛋白质组，包括一系列定量方法，细胞类型和实验条件。该分析自信地为49个蛋白质组分配了一个CCS，包括对来自同步细胞的蛋白质组的正确分类。我们预计，这种强大的细胞周期蛋白特征将是单细胞蛋白质组中细胞状态分类的关键。细胞内消化法是蛋白质组学中一种极简的样品处理方法。固定细胞被胰蛋白酶直接消化成多肽，用于LC-MS /MS。16个细胞周期群体（每个群体2500个细胞）的定量蛋白质组学。一个细胞周期特征将proteomeHD中的蛋白质组划分为细胞周期阶段。利用AMPL改进了Orbitrap Elite的多肽分析。我们为自下而上的蛋白质组学引入了一种流线型的样品处理方法，称为“细胞内消化”。固定细胞被胰蛋白酶直接消化成多肽，用于LC-MS /MS。结合AMPL，我们分析了16个未受干扰的细胞周期群体的蛋白质组，每个群体使用2500个细胞。我们确定了一个119蛋白的细胞周期特征。利用这一特征，我们展示了proteomeHD中蛋白质组在特定细胞周期阶段的无偏分类。精确的细胞周期分类对于解剖单细胞蛋白质组异质性具有重要意义。
Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number MS-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the “in-cell digest.” We combined this with averaged MS1 precursor library matching to quantitatively characterize proteomes from low cell numbers of human lymphoblasts. About 4500 proteins were detected from 2000 cells, and 2500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterized the proteome changes across 16 cell cycle states (CCSs) isolated from an asynchronous TK6 cells, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins that vary across the cell cycle. Clustering of the pseudoperiodic proteins showed abundance patterns consistent with “waves” of protein degradation in late S, at the G2&M border, midmitosis, and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the anaphase-promoting complex/cyclosome. The dataset also identifies putative anaphase-promoting complex/cyclosome substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high-confidence cell cycle–regulated proteins can be used to perform unbiased classification of proteomes into CCSs. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a CCS for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate that this robust cell cycle protein signature will be crucial for classifying cell states in single-cell proteomes. The in-cell digest is a minimalistic sample processing method for proteomics. Fixed cells are directly digested by trypsin into peptides for LC–MS/MS. Quantitative proteomes for 16 cell cycle populations (2500 cells each). A cell cycle signature classifies proteomes in proteomeHD into cell cycle phases. Peptide analysis using the Orbitrap Elite is improved by using AMPL. We introduce a streamlined sample processing method for bottom–up proteomics called the “in-cell digest.” Fixed cells are directly digested by trypsin to peptides for LC–MS/MS. Combined with AMPL, we analyze the proteomes of 16 unperturbed cell cycle populations using 2500 cells for each. We identify a 119-protein cell cycle signature. Using this signature, we show unbiased classification of proteomes in proteomeHD into specific cell cycle phases. Precise cell cycle classification will be important in dissecting single-cell proteome heterogeneity.
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影响因子：
3.7
作者：
Breig O;Baklouti F
通讯作者：
Baklouti F
影响因子：
7.7
作者：
Ly, Tony;Whigham, Arlene;Lamond, Angus I.
通讯作者：
Lamond, Angus I.
影响因子：
14.9
作者：
Kumar, Manjeet;Gouw, Marc;Gibson, Toby J.
通讯作者：
Gibson, Toby J.
DOI：
10.1016/j.bbamcr.2018.09.011
发表时间：
2018-12
期刊：
Biochimica et biophysica acta. Molecular cell research
影响因子：
--
作者：
Kernan J;Bonacci T;Emanuele MJ
通讯作者：
Emanuele MJ
影响因子：
16.6
作者：
Bakos G;Yu L;Gak IA;Roumeliotis TI;Liakopoulos D;Choudhary JS;Mansfeld J
通讯作者：
Mansfeld J