Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale
Artikel i vetenskaplig tidskrift, 2016

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


open source

high throughput



micro biology



Martin Zackrisson

Göteborgs universitet

Johan Hallin

Université Nice Sophia Antipolis (UNS)

Lars-Göran Ottosson

Göteborgs universitet

Peter Dahl

Göteborgs universitet

Esteban Fernandez-Parada

Göteborgs universitet

Erik Ländström

Göteborgs universitet

Luciano Fernandez-Ricaud

Göteborgs universitet

Petra Kaferle

Institut Jožef Stefan

Andreas Skyman

Chalmers, Rymd- och geovetenskap, Plasmafysik och fusionsenergi

Simon Stenberg

Göteborgs universitet

Stig Omholt

Norges teknisk-naturvitenskapelige universitet

Uros Petrovic

Institut Jožef Stefan

Jonas Warringer

Göteborgs universitet

Anders Blomberg

Göteborgs universitet

G3: Genes, Genomes, Genetics

21601836 (eISSN)

Vol. 6 9 3003-3014


Biokemi och molekylärbiologi

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