The physiological state of a cell is governed by a multitude of
processes and can be described by a combination of mechanical,
spatial and temporal properties. Quantifying cell dynamics at multiple
scales is essential for comprehensive studies of cellular function, and
remains a challenge for traditional end-point assays. We introduce an
efficient, non-invasive computational tool that takes time-lapse
images as input to automatically detect, segment and analyze
unlabeled live cells; the program then outputs kinematic cellular
shape and migration parameters, while simultaneously measuring
cellular stiffness and viscosity. We demonstrate the capabilities of the
program by testing it on human mesenchymal stem cells (huMSCs)
induced to differentiate towards the osteoblastic (huOB) lineage, and
T-lymphocyte cells (T cells) of naïve and stimulated phenotypes. The
program detected relative cellular stiffness differences in huMSCs
and huOBs that were comparable to those obtained with studies that
utilize atomic force microscopy; it further distinguished naïve from
stimulated T cells, based on characteristics necessary to invoke an
immune response. In summary, we introduce an integrated tool to
decipher spatiotemporal and intracellular dynamics of cells, providing
a new and alternative approach for cell characterization
