Single cell dynamic phenotyping

Abstract

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.

Properties of a Multidimensional Landscape Model for Determining Cellular Network Thermodynamics

“…Studies that involve imaging of live cells reveal that despite the observed consistency of the steady state (stationary) distribution, there can significant dynamic variability in cellular phenotypes in each cell and from one cell to another (3,(6)(7)(8)(9)(10)(11). A number of studies provide direct evidence that the distribution of phenotypes in steady state distributions is ergodic in that subsequent to a transient perturbation the population will eventually relax to the steady state distribution (3,(12)(13)(14)(15)(16); each cell or its progeny can explore all microstates on the landscape.…”

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

“…Methods standardization has improved reproducibility of many biological measurements, notably in standards used for mechanical testing of tissues (30). A recent effort to establish guidelines for live cell imaging reported an improved detection of motile phenotypes by lowering background variation due to technical artifacts (31). Similar studies addressing reproducibility of QPI measurements of cells (15,32) allow for easier adoption of optical phase-based approaches for research and eventually in the clinic.…”

Dynamic phenotypes: illustrating a single-cell odyssey

“…In order to detect the heterogeneity in the morphological phenotype response to chemical perturbations, and microenvironmental alterations, the singlecell dynamic phenotypes of heterogeneous populations were measured by live cell imaging and fluorescence time-lapse microscopy (Patsch et al 2016). The value of single-cell dynamic phenotypes is highly dependent upon the protocol of the study design and questions to be answered.…”

“…Although those studies observed phenotypic alterations of the same type individual cells, data filtering and analyses were not performed and presented on single cells. Patsch et al (2016) developed the new strategy and protocol to investigate phenotypes of single cells in the highthroughput imaging system in a different aspect of gene-and protein-based phenotype of single cells.…”