Acoustofluidic Separation of Placental Nanovesicle Subpopulations in Obstetrical Diseases

胎盘对胎儿发育和生长、母体内环境稳定以及广义上的妊娠健康至关重要。然而，我们在人类妊娠期间非侵入性探测胎盘健康的能力受到其子宫内深部位置及其高度血管组成的阻碍，使得胎盘在很大程度上难以进行安全和动态研究。尽管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推进，但这些间接方法提供了有关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，细胞或代谢水平的分辨率仍然有限。为了应对这些挑战，我们和其他人专注于从胎盘滋养层释放细胞外囊泡（EV），在人类中，这些细胞直接沐浴在母体血液中。我们专注于外泌体（现在称为小EV或sEV），微泡和凋亡泡，它们从滋养层细胞持续大量释放到母体循环中，并且在整个妊娠期间通过外周血检查可以获得。在这些EV中，我们主要关注胎盘sEV，它们携带很少由任何其他细胞类型表达的信息，并执行独特的胎盘生物学功能，如抗病毒反应。虽然信息丰富，但最近的数据表明sEV不是均匀的囊泡群体，而是包括几个亚组，定义为大sEV、小sEV和外泌体。除了它们的大小，这些sEV亚型的特征在于独特的货物。尽管最近发现的sEV亚群由于其革命性的非侵入性诊断领域的潜力而令研究人员兴奋，但sEV亚群尚未在临床环境中使用。这在很大程度上是由于与分离和隔离纳米尺寸的sEV亚群相关的困难。我们的团队现在已经开发出先进的声流体技术，旨在有效，可重复和快速地从血液中分离sEV。我们表明，我们可以将胎盘sEV分离到它们的特定亚群中，这在以前还没有完成。因此，我们提出的研究集中在人胎盘sEV亚群的产生，连同它们的RNA和蛋白质组货物一起沿着。我们认为，通过分析来自sEV亚群的这些分析物，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，我们提出了一种机器学习管道，用于探测正常和病理妊娠期间的亚sEV光谱。此外，我们将提高我们从脂蛋白中纯化sEV亚群的能力，并产生一个单一的集成装置，该装置可以在整个人类妊娠期间真实的时间内可靠地分离囊泡。我们相信，我们的自动化声流体方法以高产率，生物相容性的方式分离sEV亚群对于解锁sEV的临床实用性至关重要。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.