Lee Yi Xin Fiona, Johansson Henrik, Wood Matthew J A, El Andaloussi Samir
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.
Front Neurosci. 2019 Oct 18;13:1067. doi: 10.3389/fnins.2019.01067. eCollection 2019.
Extracellular vesicles (EVs) are nano-sized particles constitutively released from cells into all biological fluids. Interestingly, these vesicles contain genetic cargoes including proteins, RNA and bioactive lipids that can be functionally delivered and affect recipient cells. As a result, there is growing interest in studying EVs in pathological conditions, including central nervous system (CNS)-related diseases, as EVs may be used for diagnostic purposes or as therapeutic agents. However, one major bottleneck is the need for better EV purification strategies when considering complex biological sources such as serum/protein-rich media or plasma. In this study, we have performed a systematic comparison study between the current gold-standard method: ultracentrifugation, to an alternative: size-exclusion chromatography (LC), using induced pluripotent stem cell (iPSC) derived complex media as a model system. We demonstrate that LC allows for derivation of purer EVs from iPSCs, which was previously impossible with the original UC method. Importantly, our study further highlights the various drawbacks when using the conventional UC approach that lead to misinterpretation of EV data. Lastly, we describe novel data on our iPSC-EVs; how they could relate to stem cell biology and discuss their potential use as EV therapeutics for CNS diseases.
细胞外囊泡(EVs)是细胞持续释放到所有生物体液中的纳米级颗粒。有趣的是,这些囊泡包含遗传物质,包括蛋白质、RNA和生物活性脂质,它们可以在功能上传递并影响受体细胞。因此,人们对在包括中枢神经系统(CNS)相关疾病在内的病理条件下研究EVs的兴趣日益浓厚,因为EVs可用于诊断目的或作为治疗剂。然而,一个主要的瓶颈是,当考虑血清/富含蛋白质的培养基或血浆等复杂生物来源时,需要更好的EV纯化策略。在本研究中,我们使用诱导多能干细胞(iPSC)衍生的复杂培养基作为模型系统,对当前的金标准方法:超速离心,与另一种方法:尺寸排阻色谱法(LC)进行了系统的比较研究。我们证明,LC能够从iPSC中获得更纯的EVs,而这在原来的超速离心(UC)方法中是不可能实现的。重要的是,我们的研究进一步突出了使用传统UC方法时导致EV数据误解的各种缺点。最后,我们描述了关于我们的iPSC-EVs的新数据;它们如何与干细胞生物学相关,并讨论了它们作为CNS疾病的EV治疗剂的潜在用途。