Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers
| Autor: | Philipp Rosendahl, Nicole Töpfner, Carlo Vittorio Cannistraci, Yan Ge, Claudio Durán, Sara Ciucci, Jochen Guck |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Erythrocytes Reticulocytes Computer science 0206 medical engineering 68T99 Machine Learning (stat.ML) 02 engineering and technology Computational biology Quantitative Biology - Quantitative Methods Machine Learning (cs.LG) Human disease Statistics - Machine Learning Genetics Humans Cell mechanics Quantitative Methods (q-bio.QM) Image Cytometry biology Applied Mathematics Computational Biology Morpho biology.organism_classification Categorization FOS: Biological sciences Functional change Unsupervised learning Rheology Cytometry Biomarkers 020602 bioinformatics Unsupervised Machine Learning Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18:1405-1415 |
| ISSN: | 2374-0043 1545-5963 |
| DOI: | 10.1109/tcbb.2019.2945762 |
| Popis: | Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong and its impact on precision medicine is relevant. Towards this end, high-throughput techniques for cell mechanical phenotyping have been proposed to get a multidimensional biophysical characterization of single cells. With this motivation, our goal here is to investigate the extent to which an unsupervised machine learning methodology, which is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence cytometry (RT-FDC), can address the difficult task of providing label-free discrimination of reticulocytes from mature red blood cells. We focused on this problem, since the characterization of reticulocytes (their percentage and cellular features) in the blood is vital in multiple human disease conditions, especially bone-marrow disorders such as anemia and leukemia. Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells, and it represents a step forward in the development of high-throughput morpho-rheological-based methodologies for the computational categorization of single cells. Besides, our methodology can be an alternative but also a complementary method to integrate with existing cell-labelling techniques. 13 pages, 3 figures, 4 tables |
| Databáze: | OpenAIRE |
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