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dott. Federica CASELLI: Ricerche
Immagine a corredo della ricercaImmagine a corredo della ricercaMUSIC
MUltidimensional Single-cell Impedance Cytometry

funded by the Scientific Independence of Young Researchers Programme, SIR 2014
(Start date: September 23, 2015; Duration: 3 years)
 
The goal of the MUSIC project is to design and implement a label-free microfluidic system suitable for the accurate characterization of single cells, by relying only on impedance measurements.

Background and Motivation
Single cell analysis is the new frontier in “Omics” and it will be critical in the future in a variety of applications with extremely high socio-economic impact, including early cancer diagnosis, pharmaceutics and food analysis. However, a simple and cheap, yet high performance, single-cell assay system is presently lacking. Standard methods like flow cytometry­based assays require complex equipment, costly fluorescent­labeled antibodies and skilled technicians.
Label-free microfluidic devices represent an attractive alternative, able to measure cell bioelectric properties or cell biomechanical properties.
Bioelectric properties have been applied e.g. for blood cell discrimination and cell viability tests, and are easily obtainable by means of simple and low-cost multi-frequency impedance measurements.
As to
biomechanical properties, there is growing evidence that cell deformability may provide a label­free biomarker for determining e.g. metastatic potential, degree of differentiation or parasite invasion. However, microfluidic single-cell mechanics assays are far from being mature. They typically requires costly imaging systems, making the system bulky and demanding for time-consuming post-processing of massive image data, or exploit constriction channels that provide deformation indices which depend on cell size or surface properties, thus reducing their discrimination potential. Besides that, the complexity of cells makes the detection of specific cell types an inherently multidimensional problem, so that bioelectrical and biomechanical parameters, when used in combination, could provide a higher classification success rate than using electrical or mechanical parameters alone.