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

Martin Zackrisson ; Johan Hallin ; Lars-Göran Ottosson ; Peter Dahl ; Esteban Fernandez-Parada ; Erik Ländström ; Luciano Fernandez-Ricaud ; Petra Kaferle ; Andreas Skyman (Institutionen för rymd- och geovetenskap, Plasmafysik och fusionsenergi) ; Simon Stenberg ; Stig Omholt ; Uros Petrovic ; Jonas Warringer ; Anders Blomberg
G3: Genes, Genomes, Genetics (2160-1836). Vol. 6 (2016), 9, p. 3003-3014 .
[Artikel, refereegranskad vetenskaplig]

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

Nyckelord: phenomics, micro biology, genetics, high throughput, mutant, screening, open source

Denna post skapades 2016-09-13. Senast ändrad 2016-12-20.
CPL Pubid: 241679


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Institutioner (Chalmers)

Institutionen för kemi och molekylärbiologi (GU)
Institutionen för marina vetenskaper (GU)
Institutionen för rymd- och geovetenskap, Plasmafysik och fusionsenergi (2013-2017)


Bioteknisk apparatteknik
Bioanalytisk teknik

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