Acoustofluidic Separation of Placental Nanovesicle Subpopulations in Obstetrical Diseases

胎盘对胎儿的发育和生长、母体的体内平衡以及广义上的妊娠健康至关重要。然而，我们在人类怀孕期间对胎盘健康进行无创探测的能力受到其位于宫内深处和高度血管组成的阻碍，使得胎盘在很大程度上无法进行安全和动态的调查。虽然胎盘研究已经通过细胞培养、离体系统、动物模型和产后分析得到了进展，但这些间接的方法提供了关于胎盘健康的事后信息。胎盘成像已经彻底改变了胎盘医学领域，但在分子、细胞或代谢水平上的分辨率仍然有限。为了应对这些挑战，我们和其他研究人员将重点放在胎盘滋养细胞释放细胞外囊泡（ev）上，在人类中，胎盘滋养细胞直接沐浴在母体血液中。我们重点研究了外泌体（现在称为小EVs或sev）、微泡和凋亡泡，它们从滋养细胞源源不断地释放到母体循环中，并在整个妊娠期间通过外周血检查可检测到。在这些ev中，我们主要关注胎盘sev，它们包含其他细胞类型很少表达的信息，并执行独特的胎盘生物学功能，如抗病毒反应。最近的数据表明，sev并不是一个统一的囊泡群，而是由几个亚群组成，定义为大sev、小sev和外显子。除了它们的大小，这些sEV亚型的特点是独特的货物。尽管最近sEV亚群的发现使研究人员兴奋不已，因为它们有可能彻底改变非侵入性诊断领域，但sEV亚群尚未在临床环境中得到利用。这主要是由于分离和分离纳米级sEV亚群的困难。我们的团队现在已经开发出先进的声流体技术，旨在有效、可重复、快速地从血液中分离sev。我们表明，我们可以将胎盘sev分离到它们特定的亚群中，这是以前没有完成的。因此，我们提出的研究重点是人类胎盘sEV亚群的产生，以及它们的RNA和蛋白质组。我们假设，通过分析这些sEV亚群的分析物，我们可以阐明与胎盘健康相关的生物活性分子的独特景观。为了降低数据复杂性，我们提出了一个机器学习管道，用于探测正常和病理妊娠期间的亚sev光谱。此外，我们将提高从脂蛋白中纯化sEV亚群的能力，并产生一个单一的集成设备，可以在人类妊娠期间实时可靠地分离囊泡。我们相信，我们的自动化声流方法以高产量、生物相容性的方式分离sEV亚群，对于解锁sEV的临床应用至关重要。从我们的研究中获得的见解将改善妊娠期间的非侵入性诊断，并可能发现个性化胎盘治疗的新靶点。

The placenta is essential for fetal development and growth, maternal homeostasis, and broadly, pregnancy health. Yet, our ability to non-invasively probe placental health during human pregnancy is hampered by its deep intrauterine location and its highly vascular composition, rendering the placenta largely inaccessibly for safe and dynamic investigation. Whereas placental research has been advanced by cell culture, ex vivo systems, animal models, and postpartum analyses, these indirect approaches provide ex post facto information about placental health. Placental imaging has revolutionized the field of placental medicine, but resolution at the molecular, cellular, or metabolic level remains limited. To address these challenges, we and others have focused on the release of extracellular vesicles (EVs) from placental trophoblasts, which, in humans, are directly bathed in maternal blood. We focused on exosomes (now termed small EVs or sEVs), microvesicles, and apoptotic blebs, which are continuously and abundantly released from trophoblasts into the maternal circulation and are accessible throughout pregnancy by peripheral blood tests. Among these EVs, we focus mainly on placental sEVs, which harbor messages that are seldom expressed by any other cell types and execute unique placental biological functions, such as an antiviral response. While informative, recent data indicate that sEVs are not a uniform population of vesicles, but comprise several subgroups, defined as large sEVs, small sEVs, and exomeres. In addition to their size, these sEV subtypes are characterized by distinctive cargo. Although the recent discovery of sEV subpopulations has excited researchers due to their potential to revolutionize the field of non-invasive diagnostics, sEV subpopulations have yet to be utilized in clinical settings. This is largely due to the difficulties associated with separation and isolation the nano-sized sEV subpopulations. Our group has now developed advanced acoustofluidic technologies designed to effectively, reproducibly, and rapidly isolate sEVs from blood. We show that we can separate placental sEVs into their specific subpopulations, which has not been previously accomplished. Our proposed investigation therefore focuses on the production of human placental sEV subpopulations, along with their RNA and proteome cargo. We posit that, by profiling these analytes from sEV subpopulations, we can illuminate a unique landscape of bioactive molecules that are relevant to placental health. To reduce data complexity, we propose a machine learning pipeline that will be used to probe the sub-sEV spectra during normal and pathological pregnancies. Further, we will improve our ability to purify sEV subpopulations from lipoproteins, and generate a single, integrated device that can reliably separate vesicles in real time across human gestation. We believe that our automated acoustofluidic approach to separating sEV subpopulations in a high-yield, biocompatible manner is critical to unlocking the clinical utility of sEVs. Insights gained from our investigation will improve non-invasive diagnostics during pregnancy and may uncover new targets for personalized placental therapeutics.