Information Sciences in Imaging 2012 Seminar Series: July

Title: 

Inference of Cancer Cell Types By Computational Analysis of Single-Cell Measurements of Deformability and Cell-Surface Friction

Abstract: 

Historically, there has been extensive interest in identifying molecular markers (e.g. genes, transcripts, proteins) that can be used to differentiate cancer cells with high metastatic potential from cancer cells with low metastatic potential. Such markers would have significant clinical utility in diseases like lung and prostate cancer where there is a wide range of tumor behavior. Notably, these diverse molecular characteristics manifest in cells' mechanical properties. Consequently, assays of cell mechanics are therefore of significant interest both for gaining insight into cancer processes and as potential prognostic tools. 

Here we present an approach to simultaneously measure the mass and mechanical properties of individual cells using a novel adaptation of a suspended microchannel resonator (SMR) with a flow-restriction that cells must squeeze through. Unlike traditional micro-pipette aspiration or atomic force microscopy approaches, the SMR device has a throughput of several thousand cells per hour and can sample the detailed trajectory of the cell as it deforms. Using the mass, passage time, and other features of the trajectory, we are able to distinguish cells treated with actin-disrupting drugs (specifically latrunculin B) from untreated cells with high accuracy (>90%), and we can also distinguish cells with high metastatic potential from those with low-metastatic potential on a statistical basis (>70% accuracy for individual cells). We also report on preliminary efforts to connect our measured trajectories to biophysical models of cells traversing a confined channel. Measured trajectories show deviations from a classical liquid drop model with power law viscosity indicating that more sophisticated biophysical models of how cells respond to extrinsic compression and drag.