Abstract
We define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell magnetorotation, with the aid of cell embedded magnetic nanoparticles, our method allows each cell to move freely in 3 dimensions, with a rapid following of cell deformations in all 3-dimensions, so as to identify and classify a cell by its dynamic morphology. Using object recognition and machine learning algorithms, we continuously measure the real-time shape dynamics of each cell, where from we successfully resolve the inherent broad heterogeneity of the morphological phenotypes found in a given cancer cell population. In three illustrative experiments we have achieved clustering, differentiation, and identification of cells from (A) two distinct cell lines, (B) cells having gone through the epithelial-to-mesenchymal transition, and (C) cells differing only by their motility. This microfluidic method may enable a fast screening and identification of invasive cells, e.g., metastatic cancer cells, even in the absence of biomarkers, thus providing a rapid diagnostics and assessment protocol for effective personalized cancer therapy.
Highlights
Despite much progress over the last century, cancer remains one of the leading causes of death globally [1]
To examine the cellular morphodynamics, we combine cell magneto-rotation with machine learning and we show that this approach may allow one to probe both cell motility as well as morphological expression
Green fluorescent protein (GFP) expressing cancer cells are activated by endosomic uptake of magnetic nanoparticles, and are loaded into a microfluidic device that contains an array of microwells where they remain non-adherent while rotating in an oscillating magnetic field [31, 32]
Summary
Despite much progress over the last century, cancer remains one of the leading causes of death globally [1]. Green fluorescent protein (GFP) expressing cancer cells are activated by endosomic uptake of magnetic nanoparticles, and are loaded into a microfluidic device that contains an array of microwells where they remain non-adherent while rotating in an oscillating magnetic field [31, 32] This enables 3-dimensional cell deformations in which the cells explore and express their morphological phenotype. Highly migratory cells were found to be morphodynamically distinct from a control population This new machine learning (ML) based method appears to have the potential to map and classify the morphodynamic distribution of a given cell population, and to provide information on the degree of morphological plasticity of a tumor cell’s population. We have used this method here to demonstrate the strong relationship between a cell’s morphological and biological behaviors
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