Automated measurement of cell motility and proliferation

Abstract

Background: Time-lapse microscopic imaging provides a powerful approach for following changes in cell phenotype over time. Visible responses of whole cells can yield insight into functional changes that underlie physiological processes in health and disease. For example, features of cell motility accompany molecular changes that are central to the immune response, to carcinogenesis and metastasis, to wound healing and tissue regeneration, and to the myriad developmental processes that generate an organism. Previously reported image processing methods for motility analysis required custom viewing devices and manual interactions that may introduce bias, that slow throughput, and that constrain the scope of experiments in terms of the number of treatment variables, time period of observation, replication and statistical options. Here we describe a fully automated system in which images are acquired 24/7 from 384 well plates and are automatically processed to yield high-content motility and morphological data.

Spontaneous migration of cancer cells under conditions of mechanical confinement

“…15,16 Moreover, these assays rely on cell count after a defined period of time and have to take into account the proliferation of cells that occurs during the assay and subtract that from the counting of migrating cancer cells, 17 thus making the quantification of cellular motility less accurate. 18 While detailed recording and analysis of moving cells are possible in some in vitro systems, 19 these experiments are performed on flat surfaces, e.g. wound healing assay, 20 and extrapolating their relevance from 2D to the behavior of cancer cells in 3D tissues is challenging.…”

Bacterial cell identification in differential interference contrast microscopy images

“…Moreover, the use of deformable templates has been proposed [7,8]. These are modelled closed curves, which are fitted to object boundaries in iterative processes.…”

Overlap-based cell tracker

“…Automated microscopy has facilitated the large scale acquisition of live cell image data [ Sig06 , Gor07 , Dav07 , and Bah05 ]. In the case of low magnification imaging in transmission mode, the migration, morphology, and lineage development of large numbers of single cells in culture can be monitored.…”

“…Many popular cell tracking techniques are based on complex probabilistic models. In [ Bah05 ] Gaussian probability density functions are used to characterize the selected tracking criteria. In [ Mar06 ] cells are tracked by fitting their tracks to a persistent random walk model based on mean square displacement.…”