Introduction

Fluorescence time-lapse imaging has become a powerful tool to investigate complex dynamic processes such as cell division or intracellular trafficking. Automated microscopes generate time-resolved imaging data at high throughput, yet tools for quantification of large-scale movie data are largely missing. Here we present CellCognition, a computational framework to annotate complex cellular dynamics. We developed a machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states. Incorporation of time information into the annotation scheme was essential to suppress classification noise at state transitions and confusion between different functional states with similar morphology. We demonstrate generic applicability in different assays and perturbation conditions, including a candidate-based RNA interference screen for regulators of mitotic exit in human cells. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.

Publications

  1. CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging.
    Cite this
    Held M, Schmitz MH, Fischer B, Walter T, Neumann B, Olma MH, Peter M, Ellenberg J, Gerlich DW, 2010-09-01 - Nature methods

Credits

  1. Michael Held
    Developer

  2. Michael H A Schmitz
    Developer

  3. Bernd Fischer
    Developer

  4. Thomas Walter
    Developer

  5. Beate Neumann
    Developer

  6. Michael H Olma
    Developer

  7. Matthias Peter
    Developer

  8. Jan Ellenberg
    Developer

  9. Daniel W Gerlich
    Investigator

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT006829
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByDaniel W Gerlich