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Related Experiment Video

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High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
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Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale.

Martin Zackrisson1, Johan Hallin2, Lars-Göran Ottosson1

  • 1Department of Chemistry and Molecular Biology, University of Gothenburg, 40530, Sweden.

G3 (Bethesda, Md.)
|July 3, 2016
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We developed an automated microbial phenomics framework to accurately measure growth phenotypes at scale. This high-throughput system analyzes thousands of growth curves, advancing microbial research and phenomics databases.

Keywords:
geneticshigh throughputmicrobiologyphenomicsscreening

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Area of Science:

  • Microbiology
  • Systems Biology
  • Genomics

Background:

  • Phenomics, the study of traits across large populations, lags behind genomics.
  • Microbial growth estimation is crucial for understanding fitness and is a key phenotype.
  • Existing methods lack the scale and precision for comprehensive microbial phenomic studies.

Purpose of the Study:

  • To introduce an automated microbial phenomics framework for high-throughput growth phenotype analysis.
  • To enable accurate, precise, and highly resolved growth measurements at an unprecedented scale.
  • To generate high-quality data for large cohorts, advancing phenomics databases.

Main Methods:

  • Development of transmissive scanning hardware and software technology.
  • Frequent acquisition of precise colony population size measurements.
  • Extraction of population growth rates from growth curves and removal of spatial bias via reference-surface normalization.

Main Results:

  • The framework automatically records and analyzes approximately 100,000 growth curves in parallel.
  • Demonstrated the framework's utility by refining salt-defense biology in baker's yeast.
  • Achieved accurate, precise, and highly resolved microbial growth phenotypes.

Conclusions:

  • The automated microbial phenomics framework represents a significant advancement in the field.
  • Enables high-quality data generation for extensive microbial cohorts.
  • Facilitates the creation of well-populated and standardized phenomics databases for future research.