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

TY - JOUR

T1 - Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a)

AU - Giesecke, Yvonne

AU - Soete, Samuel

AU - MacKinnon, Katarzyna

AU - Tsiaras, Thanasis

AU - Ward, Madeline

AU - Althobaiti, Mohammed

AU - Suveges, Tamas

AU - Lucocq, James E.

AU - McKenna, Stephen J.

AU - Lucocq, John M.

PY - 2020/9/2

Y1 - 2020/9/2

N2 - 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 Regionbased 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.

AB - 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 Regionbased 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.

KW - lipoproteins

KW - nanoparticles

KW - low-density

KW - apolipoprotein B

KW - apolipoprotein(a)

KW - electron microscopy

KW - cardiovascular disease

KW - machine learning

KW - Apolipoprotein(a)

KW - Low-density lipoproteins

KW - Cardiovascular disease

KW - Electron microscopy

KW - Apolipoprotein B

KW - Nanoparticles

KW - Lipoproteins

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85090511295

U2 - 10.3390/ijms21176373

DO - 10.3390/ijms21176373

M3 - Article

C2 - 32887372

SN - 1661-6596

VL - 21

JO - International Journal of Molecular Sciences

JF - International Journal of Molecular Sciences

IS - 17

M1 - 6373

ER -