Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a)

International Journal of Molecular Sciences

Katarzyna MacKinnon¹  Thanasis Tsiaras¹  Tamas Suveges¹  StephenJ. McKenna¹  JamesE. Lucocq²  Samuel Soete³  Mohammed Althobaiti³  Yvonne Giesecke³  Madeline Ward³  JohnM. Lucocq³ 

[1] CVIP, School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK;Department of Orthopaedics, Ninewells Hospital, James Arrott Drive, Dundee DD1 9SY, UK;Structural Cell Biology Group, School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UK;

关键词: lipoproteins;    nanoparticles;    low-density lipoproteins;    apolipoprotein B;    apolipoprotein(a);    electron microscopy;   

DOI  :  10.3390/ijms21176373

来源: DOAJ

【 摘 要 】

Plasma lipoproteins are important carriers of cholesterol and have been linked strongly to cardiovascular disease (CVD). Our study aimed to achieve fine-grained measurements of lipoprotein subpopulations such as low-density lipoprotein (LDL), lipoprotein(a) (Lp(a), or remnant lipoproteins (RLP) using electron microscopy combined with machine learning tools from microliter samples of human plasma. In the reported method, lipoproteins were absorbed onto electron microscopy (EM) support films from diluted plasma and embedded in thin films of methyl cellulose (MC) containing mixed metal stains, providing intense edge contrast. The results show that LPs have a continuous frequency distribution of sizes, extending from LDL (> 15 nm) to intermediate density lipoprotein (IDL) and very low-density lipoproteins (VLDL). Furthermore, mixed metal staining produces striking “positive” contrast of specific antibodies attached to lipoproteins providing quantitative data on apolipoprotein(a)-positive Lp(a) or apolipoprotein B (ApoB)-positive particles. To enable automatic particle characterization, we also demonstrated efficient segmentation of lipoprotein particles using deep learning software characterized by a Mask Region-based Convolutional Neural Networks (R-CNN) architecture with transfer learning. In future, EM and machine learning could be combined with microarray deposition and automated imaging for higher throughput quantitation of lipoproteins associated with CVD risk.

【 授权许可】

Unknown