Application of 4D proteomics and super-resolution microscopy in extracellular vesicle and particle-borne biomarker discovery for early pancreatic cancer detection

项目总结/摘要 胰腺腺癌（PDAC）由于诊断较晚， 无效的治疗。细胞外囊泡和颗粒（extracellular vesicles and particles，EVP）是由包括肿瘤细胞在内的多种细胞分泌的 细胞，并包装选择性分子，包括蛋白质、脂质、核酸和代谢物。EVP是 积极释放到流通和越来越多的证据表明，循环EVP可以作为 用于早期癌症检测的生物标志物。质谱法（MS）已被广泛用于生物标志物 液体活检中的发现，包括EVP蛋白表征。然而， 所获得的信息受限于（i）分析技术的灵敏度和分辨率，以及（ii） 蛋白质组的复杂性，导致高度丰富的血清来源的污染物。本研究的目的 应用优化的可重复EVP分离方法结合非对称流场流 分离（AF 4）技术以分离具有显著提高的纯度的EVP亚群，并采用三种 新的分析技术，包括极灵敏的timsTOF 4D蛋白质组学MS，超分辨率 单个EVP的dSTORM成像分析，以及光催化邻近标记蛋白质组学（µMap）技术， 发现和验证新的循环EVP蛋白生物标志物，用于PDAC的早期检测。在目标1中， 采用无标记的timsTOF MS并使用从以下组织收集的样品（血浆和肿瘤组织）： 对于新诊断的PDAC患者，我们将鉴定新的循环EVP蛋白生物标志物， 早期疾病。将通过稳健的绝对定量测定进一步验证排名靠前的候选物 采用靶向平行反应监测（PRM）MS。在目标2中，我们将确定 在目标1中鉴定的特定EVP生物标志物蛋白在单个EVP处的表示和结构位置 通过利用超分辨率dSTORM成像分析水平。单个EVP分析的性能将 通过蛋白质印迹和/或ELISA分析与单个蛋白质靶标的批量分析进行比较。我们 将进一步探索将这种分析工具直接应用于血浆样本而无需事先分离EVP的可能性。 最后，在目标3中，我们将通过使用我们的方法来定义单个生物标志物的蛋白质-蛋白质相互作用（PPI）。 最近开发的光催化邻近标记蛋白质组学（µMap）技术。三种潜在生物标志物 在我们以前的研究中确定的候选者和在本研究中发现的新候选者目标1和目标2将 进行PPI分析。这些相互作用将通过超分辨率成像得到进一步验证 单EVP单分子水平的分析。我们将确定是否存在或不存在这些 相互作用为早期癌症检测提供了更稳健的方法。我们预测， 这些技术的结合将极大地促进胰腺癌早期检测的新生物标志物的发现。它 还探讨了EVP PPI作为一种新的生物标志物，并为开发治疗提供了依据 在未来瞄准这些互动网络。

Project Summary/Abstract Pancreatic adenocarcinoma (PDAC) ranks among the most lethal cancers due to a late diagnosis and ineffective treatments. Extracellular vesicles and particles (EVPs) are secreted by most cells, including tumor cells, and package selective molecules, including proteins, lipids, nucleic acids, and metabolites. EVPs are actively released into the circulation and mounting evidence suggests that circulating EVPs can serve as biomarkers for early cancer detection. Mass spectrometry (MS) has been extensively utilized for biomarker discovery in liquid biopsies, including EVP protein characterization. However, the scope and depth of the information obtained is limited by (i) the sensitivity and resolution of the analytic technologies and (ii) the proteomic complexity resulting from highly abundant serum-derived contaminants. The objective of this study is to apply an optimized reproducible EVP isolation method in conjunction with asymmetric-flow field-flow fractionation (AF4) technology to isolate EVP subsets with significantly improved purity and to employ three novel analytic technologies, including extremely sensitive timsTOF 4D proteomic MS, super-resolution dSTORM imaging analysis of single EVPs, and photocatalytic proximity labeling-proteomics (µMap) technology, to discover and validate novel, circulating EVP protein biomarkers for early detection of PDAC. In Aim 1, by employing the label-free timsTOF MS and using samples (blood plasma and tumor tissues) collected from patients with newly diagnosed PDAC, we will identify novel circulating EVP protein biomarkers that correlate with early stage disease. Top-ranked candidates will be further validated by robust absolute quantitation assays employing targeted parallel reaction monitoring (PRM) MS. In Aim 2, we will determine the percent representation and structural location of specific EVP biomarker proteins identified in Aim 1 at the single EVP level by utilizing the super-resolution dSTORM imaging analysis. The performance of single EVP analysis will be compared to the bulk analysis of individual protein targets via western blotting and/or ELISA analysis. We will further explore the potential to apply this analytic tool directly to plasma samples without prior EVP isolation. Lastly, in Aim 3, we will define protein-protein interactions (PPIs) of individual biomarkers by employing our recently developed photocatalytic proximity labeling-proteomics (µMap) technology. Three potential biomarker candidates identified in our previous study and novel candidates discovered in this study Aim 1 and Aim 2 will be subjected to the PPI analysis. These interactions will be further validated by super-resolution imaging analysis at the single-EVP, single-molecule level. We will establish if the presence or absence of these interactions provides a more robust approach for early cancer detection. We predict that combined application of these technologies will greatly facilitate novel biomarker discovery for early detection of pancreatic cancer. It also explores EVP PPIs as a new category of biomarkers and provide a rationale for developing therapies targeting these interactive networks in the future.