PT  - JOURNAL ARTICLE
AU  - Zackrisson, Martin
AU  - Hallin, Johan
AU  - Ottosson, Lars-Göran
AU  - Dahl, Peter
AU  - Fernandez-Parada, Esteban
AU  - Ländström, Erik
AU  - Fernandez-Ricaud, Luciano
AU  - Kaferle, Petra
AU  - Skyman, Andreas
AU  - Omholt, Stig
AU  - Petrovic, Uros
AU  - Warringer, Jonas
AU  - Blomberg, Anders
TI  - Scan-o-matic: high-resolution microbial phenomics at a massive scale
AID  - 10.1101/031443
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 031443
4099  - http://biorxiv.org/content/early/2015/11/12/031443.short
4100  - http://biorxiv.org/content/early/2015/11/12/031443.full
AB  - 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. We introduce an automated microbial phenomics framework that delivers accurate and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through introduction 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 by reference-surface normalization. Our prototype arrangement automatically records and analyses 100,000 experiments in parallel. We demonstrate the power of the approach by extending and nuancing the known salt defence biology in baker’s yeast. The introduced framework will have a transformative impact by providing high-quality microbial phenomics data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases.