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G3 (Bethesda). 2016 Sep 8;6(9):3003-14. doi: 10.1534/g3.116.032342.

Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale.

Author information

1
Department of Chemistry and Molecular Biology, University of Gothenburg, 40530, Sweden.
2
IRCAN, CNRS UMR 6267, INSERM U998, University of Nice, 06107, France.
3
Department of Marine Sciences, University of Gothenburg, 40530, Sweden.
4
Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia.
5
Department of Earth and Space Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
6
Department of Chemistry and Molecular Biology, University of Gothenburg, 40530, Sweden Department of Animal and Aquacultural Sciences, Centre for Integrative Genetics (CIGENE), Norwegian University of Life Sciences (UMB), 1432 Ås, Norway.
7
Department of Biotechnology, Faculty of Natural Sciences and Technology, NTNU Norwegian University of Science and Technology, N-7491 Trondheim, Norway.
8
Department of Marine Sciences, University of Gothenburg, 40530, Sweden anders.blomberg@marine.gu.se.

Abstract

The capacity to map traits over large cohorts of individuals-phenomics-lags far behind the explosive development in genomics. For microbes, the estimation of growth is the key phenotype because of its link to fitness. We introduce an automated microbial phenomics framework that delivers accurate, precise, and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through the introduction of transmissive scanning hardware and software technology, frequent acquisition of exact colony population size measurements, extraction of population growth rates from growth curves, and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyzes close to 100,000 growth curves in parallel. We demonstrate the power of the approach by extending and nuancing the known salt-defense biology in baker's yeast. The introduced framework represents a major advance in microbial phenomics by providing high-quality data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases.

KEYWORDS:

genetics; high throughput; microbiology; phenomics; screening

PMID:
27371952
PMCID:
PMC5015956
DOI:
10.1534/g3.116.032342
[Indexed for MEDLINE]
Free PMC Article

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