Real-Time Nanoscale Multiparametric Biophysical Phenotyping of Single Cells by Surface Plasmon Resonance Microscopy

Abstract

How cell physical state relates to function and stimulus response remains difficult to resolve because most methods measure only one biophysical property at a time. Yet cellular behavior emerges from the interplay of features, including adhesion, morphology, and mechanical dynamics. Building on previous surface plasmon resonance microscopy (SPRM) and related plasmonic microscopy approaches, we developed an SPRM platform for label-free, real-time multiparametric phenotyping of single live cells. By probing the cell–substrate interface with nanometer-scale sensitivity, the platform jointly quantifies three complementary descriptors from the same time-resolved image sequence: adhesion-associated SPR intensity (I), contact area (A), and effective spring constant (k). Applied to Entamoeba histolytica, a highly deformable protozoan parasite, this approach resolved baseline physical states, drug-induced changes, and interaction-dependent responses to bacteria and other cells. Machine learning further classified early single-cell phenotypes relative to treatment-defined viable-reference and non-viable-reference groups. These results establish a label-free SPRM workflow for resolving early interface-associated phenotypic changes in cells during drug exposure and cell-interaction assays.