MoMA
MoMA performs long-term quantitative tracking of single-cell growth and gene expression from phase-contrast images to study gene regulatory interactions and cell-fate decisions using the dual-input Mother Machine (DIMM).
Key Features:
- Microfluidic Chip Integration: Integration with the dual-input Mother Machine (DIMM) enables controlled and continuous variation of external conditions during single-cell experiments.
- Phase-contrast image-based tracking: Long-term quantitative tracking of growth and gene expression is performed using phase-contrast images.
- High-accuracy segmentation and tracking: High-accuracy segmentation and tracking of individual cells over extended periods leverages the geometry of the microfluidic device and does not require additional staining or labeling.
- Leveraged editing procedures: Leveraged editing procedures enable bulk correction of related segmentation and tracking errors.
Scientific Applications:
- Single-cell gene regulation and fate decisions: Enables analysis of gene regulatory interactions and cell-fate decisions at the single-cell level.
- Dynamic gene regulatory networks: Facilitates study of dynamic gene regulatory networks and cellular responses to controlled environmental stimuli.
- Escherichia coli lac operon studies: Has been used to investigate induction of the Escherichia coli lac operon in response to a switch from glucose to lactose.
Methodology:
MoMA processes phase-contrast images with segmentation and tracking algorithms that leverage microfluidic device geometry and applies leveraged editing procedures for error correction.
Topics
Details
- Tool Type:
- desktop application, plugin
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- Shell
- Added:
- 7/5/2018
- Last Updated:
- 11/25/2024
Operations
Publications
Kaiser M, Jug F, Julou T, Deshpande S, Pfohl T, Silander OK, Myers G, van Nimwegen E. Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software. Nature Communications. 2018;9(1). doi:10.1038/s41467-017-02505-0. PMID:29335514. PMCID:PMC5768764.