Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters. Issue 24 (18th November 2022)

• Record Type: Journal Article
• Authors: Maremonti, Maria Isabella; Dannhauser, David; Panzetta, Valeria; Netti, Paolo Antonio; Causa, Filippo
• Is Part Of: Lab on a chip. Volume 22:Issue 24(2022)
• Page Start: 4871
• Page End: 4881
• Publication Date: 2022-11-18
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/d2lc00902a
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : This work demonstrates how a small set of motion parameters uniquely measures a wide range of cell deformability in microfluidics. Abstract : Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where a large heterogeneity of rheological/mechanical properties is present. Therefore, a simple, versatile and cost-effective recognition method for variable rheological/mechanical properties of cells is needed. Here, we introduce a new set of in-flow motion parameters capable of identifying heterogeneity among cell deformability, properly modified by the administration of drugs for cytoskeleton destabilization. Firstly, we measured cell deformability by identification of in-flow motions, rolling (R), tumbling (T), swinging (S) and tank-treading (TT), distinctively associated with cell rheological/mechanical properties. Secondly, from a pool of motion and structural cell parameters, an unsupervised machine learning approach based on principal component analysis (PCA) revealed dominant features: the local cell velocity ( V Cell / V Avg ), the equilibrium position ( Y Eq ) and the orientation angle variation (Δ φ ). These motion parameters clearly defined cell clusters in terms of motion regimes corresponding to specific deformability. Such correlation is verified in a wide range of rheological/mechanical properties from the elastic cells moving like R until the almost viscous cells moving as TT. Thus, our approach shows how simple motion parameters allow cell deformability heterogeneity recognition, directly measuring rheological/mechanical properties.