Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers

Ge Y, Rosendahl P, Duran C, Toepfner N, Ciucci S, Guck J, Cannistraci CV (2021)

Publication Type: Journal article

Publication year: 2021

Journal

IEEE-ACM Transactions on Computational Biology and Bioinformatics Institute of Electrical and Electronics Engineers (IEEE) / Association for Computing Machinery (ACM)

Book Volume: 18

Pages Range: 1405-1415

Article Number: 8884110

Journal Issue: 4

DOI: 10.1109/TCBB.2019.2945762

Abstract

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.

Authors with CRIS profile

Jochen Guck

Involved external institutions

Technische Universität Dresden

Germany (DE)

How to cite

APA:

Ge, Y., Rosendahl, P., Duran, C., Toepfner, N., Ciucci, S., Guck, J., & Cannistraci, C.V. (2021). Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 18(4), 1405-1415. https://doi.org/10.1109/TCBB.2019.2945762

MLA:

Ge, Yan, et al. "Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers." IEEE-ACM Transactions on Computational Biology and Bioinformatics 18.4 (2021): 1405-1415.

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