Supporting data of “A Chemical Biology Approach for Cell Type-Specific Proteomic and Interactomic Profiling and the Identification of Neuroprotective HSP90 Client Proteins”

To simultaneously profile the proteome and interactome in specific cells ofcomplex tissues, we developed a chemical biology technique called MACSPI(Methionine Analog-based Cell-Specific Proteomics and Interactomics) anddemonstrated its utilities in the popular multicellular model organism C. elegans.Our approach involved utilizing an engineered methionine analog equipped with abio-orthogonal tag and a photo-crosslinker, along with cell-specific expression of amutant methionyl-tRNA synthetase (MetRS) capable of incorporating thismethionine analog into proteins. As a proof-of-concept, we profiled the proteomesand interactomes of the body wall muscles and the nervous system of C. elegans.Using click-chemistry, protein enrichment, and LC-MS/MS, we confirmed that our 3probe selectively labels proteins in target cells, enabling their proteomic profilingwithin whole-worm lysates. To capture transient protein-protein interactions (PPIs),we combined UV-induced photo-crosslinking with a dual-enrichment strategy. Bytagging endogenous chaperones like HSP90 with HA, we performed sequentialpurifications via both the bio-orthogonal handle and the HA epitope, successfullyidentifying precise, tissue-specific chaperone interactomes.We further optimized MACSPI for specific types of neurons, such as the eightdopaminergic (DA) neurons and six touch receptor neurons (TRNs), by increasingprobe concentration, labeling generations, and click chemistry reagentsconcentrations. This deep profiling revealed a poor correlation betweentranscriptomic and proteomic levels, underscoring the need for direct proteinmeasurement. Furthermore, to decipher the mechanism of neurodegenerativediseases like Parkinson's disease (PD), which increasingly affects the human agingpopulation, and to identify key genes with neuroprotective functions, we mappedproteomic and transcriptomic alterations in a PD model and compared those withour HSP90 interactome data to identify HSP90 client proteins that may haveneuroprotective effects and tested them in functional assays. Additionally, weprofiled the proteomic alterations induced by colchicine treatment, which causesmicrotubule depolymerization in C. elegans, and found that microtubule stabilitycontrols the protein level of many proteins. Ultimately, our work establishes apowerful platform for tissue-resolution interactomics and cell-resolutionproteomics, aiming to decipher the disease mechanism and identify key genes withneuroprotective functions based on the data obtained.
The "Dataset for quantification" includes all the analyzed MS data. This dataset provides the data for the correlation analysis and the volcano plotting. "Dataset for pathway enrichment" dataset includes all the downstream analysis followed by the "Dataset for quantification". This dataset include the KEGG and GO term enrichment analysis.

为同时解析复杂组织中特定细胞的蛋白质组与相互作用组，我们开发了一种名为MACSPI（基于甲硫氨酸类似物的细胞特异性蛋白质组学与相互作用组学，Methionine Analog-based Cell-Specific Proteomics and Interactomics）的化学生物学技术，并在经典多细胞模式生物秀丽隐杆线虫（C. elegans）中验证了其应用潜力。我们的方法依托一款带有生物正交标签与光交联基团的工程化甲硫氨酸类似物，结合可将该类似物掺入蛋白质的突变型甲硫氨酰-tRNA合成酶（MetRS）的细胞特异性表达。作为概念验证，我们对秀丽隐杆线虫的体壁肌肉与神经系统的蛋白质组和相互作用组进行了表征。借助点击化学、蛋白质富集与液相色谱-串联质谱（LC-MS/MS）技术，我们证实该探针可选择性标记靶细胞内的蛋白质，从而实现在全蠕虫裂解物中对靶细胞蛋白质组的分析。为捕获瞬时蛋白质-蛋白质相互作用（PPI），我们将紫外诱导光交联与双富集策略相结合。通过将内源性伴侣蛋白（如热休克蛋白90（HSP90））标记为HA表位标签，我们借助生物正交手柄与HA表位依次完成纯化，成功鉴定出精准的组织特异性伴侣蛋白相互作用组。我们进一步针对特定类型神经元（如8个多巴胺能（DA）神经元与6个触觉受体神经元（TRNs））优化了MACSPI技术，通过提高探针浓度、标记代数与点击化学试剂浓度实现了优化。此次深度表征揭示了转录组与蛋白质组水平间的弱相关性，凸显了直接蛋白质检测的必要性。此外，为解析日益影响人类衰老群体的帕金森病（PD）等神经退行性疾病的发病机制，并鉴定具有神经保护功能的关键基因，我们对帕金森病模型的蛋白质组与转录组变化进行了表征，并将其与我们的HSP90相互作用组数据进行比对，以筛选可能具有神经保护作用的HSP90客户蛋白，随后通过功能实验对其进行验证。我们还对秋水仙素处理秀丽隐杆线虫后诱导的蛋白质组变化进行了表征——秋水仙素可引发线虫体内微管解聚，研究发现微管稳定性调控众多蛋白质的表达水平。最终，本研究建立了一套可实现组织分辨率相互作用组学与细胞分辨率蛋白质组学研究的强大平台，旨在依托所获数据解析疾病机制并筛选具有神经保护功能的关键基因。本研究的“定量数据集（Dataset for quantification）”包含所有已分析的质谱数据，可为相关性分析与火山图绘制提供数据支持。“通路富集数据集（Dataset for pathway enrichment）”则包含“定量数据集”后续的所有下游分析结果，涵盖京都基因与基因组百科全书（KEGG）与基因本体（GO）术语富集分析。