Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a)
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) usin...
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| Language: | English |
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MDPI AG
2020-09-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/21/17/6373 |
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| author | Yvonne Giesecke Samuel Soete Katarzyna MacKinnon Thanasis Tsiaras Madeline Ward Mohammed Althobaiti Tamas Suveges James E. Lucocq Stephen J. McKenna John M. Lucocq |
| author_facet | Yvonne Giesecke Samuel Soete Katarzyna MacKinnon Thanasis Tsiaras Madeline Ward Mohammed Althobaiti Tamas Suveges James E. Lucocq Stephen J. McKenna John M. Lucocq |
| author_sort | Yvonne Giesecke |
| collection | DOAJ |
| description | 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 <i>Mask Region-based Convolutional Neural Networks (R-CNN)</i> 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. |
| first_indexed | 2024-03-10T16:38:10Z |
| format | Article |
| id | doaj.art-c0877c35ee4644b999172dfbd4f65d5e |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T16:38:10Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-c0877c35ee4644b999172dfbd4f65d5e2023-11-20T12:18:38ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672020-09-012117637310.3390/ijms21176373Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a)Yvonne Giesecke0Samuel Soete1Katarzyna MacKinnon2Thanasis Tsiaras3Madeline Ward4Mohammed Althobaiti5Tamas Suveges6James E. Lucocq7Stephen J. McKenna8John M. Lucocq9Structural Cell Biology Group, School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UKStructural Cell Biology Group, School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UKCVIP, School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UKCVIP, School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UKStructural Cell Biology Group, School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UKStructural Cell Biology Group, School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UKCVIP, School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UKDepartment of Orthopaedics, Ninewells Hospital, James Arrott Drive, Dundee DD1 9SY, UKCVIP, School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UKStructural Cell Biology Group, School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UKPlasma 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 <i>Mask Region-based Convolutional Neural Networks (R-CNN)</i> 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.https://www.mdpi.com/1422-0067/21/17/6373lipoproteinsnanoparticleslow-density lipoproteinsapolipoprotein Bapolipoprotein(a)electron microscopy |
| spellingShingle | Yvonne Giesecke Samuel Soete Katarzyna MacKinnon Thanasis Tsiaras Madeline Ward Mohammed Althobaiti Tamas Suveges James E. Lucocq Stephen J. McKenna John M. Lucocq 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 lipoproteins nanoparticles low-density lipoproteins apolipoprotein B apolipoprotein(a) electron microscopy |
| title | Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a) |
| title_full | Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a) |
| title_fullStr | Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a) |
| title_full_unstemmed | Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a) |
| title_short | Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a) |
| title_sort | developing electron microscopy tools for profiling plasma lipoproteins using methyl cellulose embedment machine learning and immunodetection of apolipoprotein b and apolipoprotein a |
| topic | lipoproteins nanoparticles low-density lipoproteins apolipoprotein B apolipoprotein(a) electron microscopy |
| url | https://www.mdpi.com/1422-0067/21/17/6373 |
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