Biophysical cell properties are a powerful tool for the label-free classification of cells. Here, we report a single cell investigation approach to detect different peripheral blood cell classes in microfluidics using a machine learning based optical cell signatures detection. The utilized microfluidic platform aligns cells in flow and alternatively allows compression and subsequent investigations of cells in flow. In fact, the presented approach can be interesting for a wide spectrum of clinical and diagnostic single cell investigations, such as the detection of circulating tumour cells in liquid biopsy samples.
Microfluidic platform for cell classification from optical signatures via machine learning / Dannhauser, D.; Maremonti, M. I.; Netti, P. A.; Causa, F.. - (2023). (Intervento presentato al convegno 8th National Congress of Bioengineering, GNB 2023 tenutosi a ita nel 2023).
Microfluidic platform for cell classification from optical signatures via machine learning
Dannhauser D.
;Maremonti M. I.;Netti P. A.;
2023
Abstract
Biophysical cell properties are a powerful tool for the label-free classification of cells. Here, we report a single cell investigation approach to detect different peripheral blood cell classes in microfluidics using a machine learning based optical cell signatures detection. The utilized microfluidic platform aligns cells in flow and alternatively allows compression and subsequent investigations of cells in flow. In fact, the presented approach can be interesting for a wide spectrum of clinical and diagnostic single cell investigations, such as the detection of circulating tumour cells in liquid biopsy samples.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
