3D measurement of fouling in membrane filtration: towards model based investigation and design

生物过程挑战该项目寻求解决膜过滤系统的污染是一个日益严重的问题，因为上游滴定和相关的负担增加[1]。这些复杂的进料包含大量不需要的材料，它们可能会通过几种污染机制来有害地影响性能，这些机制可以减少透过膜结构的渗透并改变筛分系数[2]。在这里，我们的目标是使用测量和可视化方法更好地理解这些机制，该方法将为以实现改进的膜设计和操作为核心的建模和模拟工作提供信息。目标-改进成像方法以测量代表当前生物处理挑战的膜和进料类型内的污物位置-结合共聚焦显微镜和X射线CT技术以新的分辨率水平确定膜内污物的位置和种类。-在多个长度尺度上使用这些方法，从最初的小样品到褶皱薄片滤筒和单通道TFF系统，以确定放大方法的影响。-接口通过建模方法获得的三维数据。项目说明过滤过程中的堆积是工业生物过程的常见问题[1，2]，需要考虑各种目标产品尺寸，必须去除杂质。在伦敦大学学院和COE内部，各种高分辨率成像技术已被应用于可视化和表征PAL分离介质，包括X射线CT、共聚焦显微镜、电子显微镜和聚焦离子束显微镜[3-5]。这表明，以互补的方式利用多种技术可以克服单个方法的缺点，并最大限度地利用收集的关于样本的数据。在这个项目中，共聚焦显微镜和超荧光显微镜将分别在标记饲料上进行光学切片和大视场成像，以确定不同特性(如孔径和不对称性)的Pall滤膜中包裹物的位置和组成。这项研究将建立在COE MSc项目的基础上，该项目使用应用于双层膜系统的共聚焦显微镜来研究脂质体在PAL膜中的定位。它将通过使用不同大小和组成的脂质体材料来进一步模拟慢病毒和潜在污染物等产品，例如可以在成像前单独标记的细胞外小泡。这些模拟将被设计成与其他COE EngD项目一致，例如在慢病毒制造中，所产生的数据将支持COE愿景。(2014)，[2]，Fallahianbijan等人。(2019)[3]，Johnson等人。(2017)[4]Johnson等人。(2018)，[5]杰克逊等人。(2014)建议时间表第1年：成像和分析培训(例如共聚焦)，使用脂质体进行初始膜实验第2年：扩展介质(包括双层)和制剂(转向模拟慢病毒的脂质体)第3年：将结果与COE中的其他成像数据结合起来，调查多长度尺度成像第4年：完成研究，将Lenti模拟与实际比较，COE成像协作，提交论文

Bioprocess challenge the project seeks to address Fouling of membrane filtration systems is an ever-increasing issue due to upstream titre and associated burden increases [1]. These complex feeds contain a large array of unwanted material that can detrimentally affect performance through several fouling mechanisms that can reduce permeation through membrane structure and change sieving coefficients [2]. Here we aim to develop a better understanding of these mechanisms using a measurement and visualisation approach that will inform modelling and simulation efforts core the CoE vision of enabling improved membrane design and operation.Objectives- To advance imaging approaches to measure foulant locations within membrane and feed types representative of current bioprocessing challenges - To combine confocal microscopy and x-ray CT techniques to determine at new levels of resolution the location and species of foulant within membranes.- Use these methods at multiple length scales, from small samples initially up to pleated sheet cartridges and single pass TFF systems to determine the impact of scale-up approach.- Interface the three dimensional data acquired with modelling approaches.Project DescriptionFouling during filtration is a commonplace issue for industrial bioprocesses [1, 2], with a variety of target product sizes to consider and impurities that must be removed. At UCL and within the CoE various high-resolution imaging techniques have been applied to visualise and characterise Pall separation media, including x-ray CT, confocal microscopy, electron microscopy and focused ion beam microscopy [3-5]. This has shown that utilising multiple techniques in a complementary manner can overcome the shortcomings of individual methods and maximise the data gathered about a sample. In this project confocal and super-fluorescence microscopy, will enable optical slicing and large field of view imaging respectively on tagged feeds to identify the location and composition of entrapped material within Pall filtration membranes of varying characteristics such as pore size and asymmetry.The research would build upon a CoE MSc project that investigated the location liposome entrapment within Pall membranes using confocal microscopy applied to dual layer membrane systems. It would further this by using liposome material with differing sizes and compositions in order to mimic products such as lentivirus and potential foulants e.g. extracellular vesicles that could be individually labelled prior to imaging. These mimics will be designed to align with other CoE EngD projects such as in lentivirus manufacture, and the data generated will support the CoE vision.[1] Gronemeyer et al. (2014), [2], Fallahianbijan et al. (2019) [3], Johnson et al. (2017)[4] Johnson et al. (2018), [5] Jackson et al. (2014)Proposed timelineYear 1: Imaging and analysis training (e.g. confocal), initial membrane experiments with liposomesYear 2: Expand media (including dual layer) and formulation (move to liposome mimic of lentivirus) Year 3: Combine results with other imaging data in the CoE, investigate multi-length scale imagingYear 4: Complete studies, compare lenti mimic to actual, CoE imaging collaboration, submit thesis