  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.
