Multiparametric single-cell biophysical cytometry under tunable viscoelastic extensional flows for classification of T-cell lymphomas on their nuclear phenotypes.

Cutaneous T-cell lymphoma cells expand in the skin microenvironment but are eliminated by therapies that act in the blood, highlighting the need to enhance cell migration from skin to blood by modulating their biomechanics to improve the efficacy of therapies. Herein, single-cell biophysical cytometry under tunable viscoelastic extensional flows to modulate the geometry for cell deformation is utilized to correlate nuclear phenotypes (size, shape, lamin protein expression and telomere organization) of clonally related lymphoma cells from the blood (Mac-1) and skin (Mac-2A and Mac-2B) to their deformability characteristics. Through coupling single-cell metrics from impedance, deformability and recovery dynamics, we infer that Mac-2A cells from the skin with larger nuclear sizes and diverse nuclear shapes exhibit lower deformability than Mac-1 cells from blood. On the other hand, through lowering nuclear lamin A/C levels for promoting a spherical telomere organization, the deformability of skin-derived Mac-2B cells is enhanced versus Mac-2A cells, despite comparable nuclear sizes and nuclear shape diversity. During recovery post-deformation, the highly deformable Mac-1 and Mac-2B cells show bullet shapes, likely due to viscous energy storage, whereas the less deformable Mac-2A cells relax to circular shapes. Using cellular metrics from >1000 events with corresponding impedance, deformation and recovery data, classification accuracies of 84.8 % can be obtained between the respective cell types using the support vector machine learning model. In this manner, the interplay of cellular biophysical characteristics can be coupled with their expression of key adhesion molecules to identify modulators that enhance cell migration to improve the efficacy of therapies.