title: 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.
authors.affiliation: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.
authors.forename: Katherin
authors.initials: K
authors.lastname: Patsch
authors.affiliation: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.
authors.forename: Chi-Li
authors.initials: CL
authors.lastname: Chiu
authors.affiliation: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Engeln
authors.affiliation: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.
authors.forename: David B
authors.initials: DB
authors.lastname: Agus
authors.affiliation: Department of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University, Palo Alto, CA, USA.
authors.forename: Parag
authors.initials: P
authors.lastname: Mallick
authors.affiliation: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.
authors.forename: Shannon M
authors.initials: SM
authors.lastname: Mumenthaler
authors.affiliation: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ruderman
mesh_terms: D016477:Artifacts; D002460:Cell Line; D054443:Cell Migration Assays / Q000379:methods; D049452:Green Fluorescent Proteins / Q000235:genetics / Q000378:metabolism; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009685:Nuclear Envelope / Q000378:metabolism; D010641:Phenotype; D011506:Proteins / Q000378:metabolism; D015203:Reproducibility of Results; D059010:Single-Cell Analysis / Q000379:methods*; D012984:Software; D057188:Workflow
chemical_list: D011506:Proteins; D049452:Green Fluorescent Proteins