Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform.
Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform.
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DOI：
10.1016/j.mcpro.2022.100491
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发表时间：
2023-02
影响因子：
7
通讯作者：
Zhou, Mowei
中科院分区：
文献类型：
--
作者：
Liao, Yen -Chen;Fulcher, James M.;Degnan, David J.;Williams, Sarah M.;Bramer, Lisa M.;Velickovic, Dusan;Zemaitis, Kevin J.;Velickovic, Marija;Sontag, Ryan L.;Moore, Ronald J.;Pasa-Tolic, Ljiljana;Zhu, Ying;Zhou, Mowei
传统的蛋白质组学方法测量来自混合细胞群或大块组织的平均信号，导致可能用作重要生物标志物的细胞亚群产生的信号稀释。自下而上蛋白质组学的最新发展使组织微环境中细胞异质性的空间映射成为可能。然而，自下而上的蛋白质组学不能明确地定义和量化蛋白质型，其是完整的（即，捕获遗传变异、可变剪接转录物和翻译后修饰的蛋白质的功能性）形式。在此，我们描述了一个空间分辨的自上而下的蛋白质组学（TDP）平台，用于直接从组织切片中进行蛋白质组鉴定和定量。空间TDP平台由一个用于痕量样品的样品制备系统和一个基于激光捕获显微切割的细胞分离系统组成。我们通过在提取缓冲液中加入benzonase来提高核蛋白的覆盖率，从而改进了用于痕量样品样品制备的一锅纳米液处理。使用200个培养细胞作为测试样品，这种方法将总蛋白质组鉴定从493增加到700;新鉴定的蛋白质组主要对应于核蛋白。为了展示组织样本中的空间TDP平台，我们分析了来自大鼠大脑皮层和下丘脑区域的激光捕获显微切割分离的组织体素。我们在基于自上而下质谱的蛋白质组鉴定和表征以及TDPortal鉴定的结合中定量了509种蛋白质组，以与蛋白质质量提取器的特征相匹配。对应于相同基因的几种蛋白质型在两个组织区域之间表现出混合的丰度分布，表明潜在的翻译后修饰特异性空间分布。空间TDP工作流程具有从小组织切片在蛋白形式水平发现生物标志物的前景。证明了小组织切片的自上而下的蛋白质组学。检测到大鼠脑内解剖区域特异性的蛋白质形式。提出了一种用于蛋白质组定量的集成信息学工作流程。我们展示了一个空间自上而下的蛋白质组学（TDP）的工作流程，为小鼠脑组织切片对应于从不同的解剖区域的200个细胞，以了解细胞的异质性。实验方案使用激光捕获显微切割-nanoPOTS平台，用于从组织中提取的蛋白质型的液相色谱质谱分析。使用集成多个TDP软件的信息学管道分析9个点，得到509个可定量的蛋白质型。从蛋白质水平测量不容易获得的蛋白质形式的独特变化可能揭示生物标志物发现的新机制。
Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples–based sample preparation system and an laser capture microdissection–based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection–isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry–based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification–specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections. Top-down proteomics of small tissue sections was demonstrated. Proteoforms specific to anatomical regions in rat brain were detected. An integrated informatics workflow for proteoform quantitation was presented. We demonstrated a spatial top-down proteomics (TDP) workflow for small rat brain tissue sections corresponding to ∼200 cells from distinct anatomical regions for understanding the cellular heterogeneity. The experimental protocol used a laser capture microdissection—nanoPOTS platform for liquid chromatography mass spectrometry of proteoforms extracted from tissue. Nine spots were analyzed using an informatics pipeline integrating multiple TDP software, yielding 509 quantifiable proteoforms. Distinct changes of proteoforms not easily accessible from protein-level measurements could reveal new mechanisms for biomarker discovery.
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影响因子：
5.5
作者：
Chen L;Kashina A
通讯作者：
Kashina A
影响因子：
7.4
作者：
Hale OJ;Cooper HJ
通讯作者：
Cooper HJ
影响因子：
4.4
作者：
Drown, Bryon S.;Jooss, Kevin;Melani, Rafael D.;Lloyd-Jones, Cameron;Camarillo, Jeannie M.;Kelleher, Neil L.
通讯作者：
Kelleher, Neil L.
影响因子：
4.4
作者：
Cox, Juergen;Neuhauser, Nadin;Mann, Matthias
通讯作者：
Mann, Matthias
影响因子：
2.3
作者：
Judd, Audra M.;Gutierrez, Danielle B.;Caprioli, Richard M.
通讯作者：
Caprioli, Richard M.