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

Research output: Contribution to journalResearch articleContributedpeer-review

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.

Details

Original languageEnglish
Article number8884110
Pages (from-to)1405-1415
Number of pages11
JournalIEEE/ACM transactions on computational biology and bioinformatics
Volume18
Issue number4
Publication statusPublished - 1 Jul 2021
Peer-reviewedYes

External IDs

PubMed 31670675

Keywords

Sustainable Development Goals

Keywords

  • cell mechanics, Fluorescence marker, marker prediction, mature red blood cell, PC-corr, real-time deformability and fluorescence cytometry, reticulocyte, unsupervised machine learning