Literature DB >> 31157778

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER.

Zachary J Heins1, Christopher P Mancuso1, Szilvia Kiriakov2, Brandon G Wong1, Caleb J Bashor3, Ahmad S Khalil4.   

Abstract

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring fitness phenotypes and improving our understanding of how genotypes are shaped by selection. Extensive recent efforts to develop and apply niche continuous culture devices have revealed the benefits of conducting new forms of cell culture control. This includes defining custom selection pressures and increasing throughput for studies ranging from long-term experimental evolution to genome-wide library selections and synthetic gene circuit characterization. The eVOLVER platform was recently developed to meet this growing demand: a continuous culture platform with a high degree of scalability, flexibility, and automation. eVOLVER provides a single standardizing platform that can be (re)-configured and scaled with minimal effort to perform many different types of high-throughput or multi-dimensional growth selection experiments. Here, a protocol is presented to provide users of the eVOLVER framework a description for configuring the system to conduct a custom, large-scale continuous growth experiment. Specifically, the protocol guides users on how to program the system to multiplex two selection pressures - temperature and osmolarity - across many eVOLVER vials in order to quantify fitness landscapes of Saccharomyces cerevisiae mutants at fine resolution. We show how the device can be configured both programmatically, through its open-source web-based software, and physically, by arranging fluidic and hardware layouts. The process of physically setting up the device, programming the culture routine, monitoring and interacting with the experiment in real-time over the internet, sampling vials for subsequent offline analysis, and post experiment data analysis are detailed. This should serve as a starting point for researchers across diverse disciplines to apply eVOLVER in the design of their own complex and high-throughput cell growth experiments to study and manipulate biological systems.

Entities:  

Year:  2019        PMID: 31157778      PMCID: PMC6738936          DOI: 10.3791/59652

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  33 in total

1.  Selection analyses of insertional mutants using subgenic-resolution arrays.

Authors:  V Badarinarayana; P W Estep; J Shendure; J Edwards; S Tavazoie; F Lam; G M Church
Journal:  Nat Biotechnol       Date:  2001-11       Impact factor: 54.908

2.  Genetic circuit performance under conditions relevant for industrial bioreactors.

Authors:  Felix Moser; Nicolette J Broers; Sybe Hartmans; Alvin Tamsir; Richard Kerkman; Johannes A Roubos; Roel Bovenberg; Christopher A Voigt
Journal:  ACS Synth Biol       Date:  2012-11-05       Impact factor: 5.110

3.  Adaptation, Clonal Interference, and Frequency-Dependent Interactions in a Long-Term Evolution Experiment with Escherichia coli.

Authors:  Rohan Maddamsetti; Richard E Lenski; Jeffrey E Barrick
Journal:  Genetics       Date:  2015-04-24       Impact factor: 4.562

4.  An Accessible Continuous-Culture Turbidostat for Pooled Analysis of Complex Libraries.

Authors:  Anna M McGeachy; Zuriah A Meacham; Nicholas T Ingolia
Journal:  ACS Synth Biol       Date:  2019-04-02       Impact factor: 5.110

5.  Multiple high-throughput analyses monitor the response of E. coli to perturbations.

Authors:  Nobuyoshi Ishii; Kenji Nakahigashi; Tomoya Baba; Martin Robert; Tomoyoshi Soga; Akio Kanai; Takashi Hirasawa; Miki Naba; Kenta Hirai; Aminul Hoque; Pei Yee Ho; Yuji Kakazu; Kaori Sugawara; Saori Igarashi; Satoshi Harada; Takeshi Masuda; Naoyuki Sugiyama; Takashi Togashi; Miki Hasegawa; Yuki Takai; Katsuyuki Yugi; Kazuharu Arakawa; Nayuta Iwata; Yoshihiro Toya; Yoichi Nakayama; Takaaki Nishioka; Kazuyuki Shimizu; Hirotada Mori; Masaru Tomita
Journal:  Science       Date:  2007-03-22       Impact factor: 47.728

6.  Scalable, Continuous Evolution of Genes at Mutation Rates above Genomic Error Thresholds.

Authors:  Arjun Ravikumar; Garri A Arzumanyan; Muaeen K A Obadi; Alex A Javanpour; Chang C Liu
Journal:  Cell       Date:  2018-11-08       Impact factor: 41.582

7.  Hunger artists: yeast adapted to carbon limitation show trade-offs under carbon sufficiency.

Authors:  Jared W Wenger; Jeffrey Piotrowski; Saisubramanian Nagarajan; Kami Chiotti; Gavin Sherlock; Frank Rosenzweig
Journal:  PLoS Genet       Date:  2011-08-04       Impact factor: 5.917

8.  Experimental Evolution Reveals Favored Adaptive Routes to Cell Aggregation in Yeast.

Authors:  Elyse A Hope; Clara J Amorosi; Aaron W Miller; Kolena Dang; Caiti Smukowski Heil; Maitreya J Dunham
Journal:  Genetics       Date:  2017-04-26       Impact factor: 4.562

9.  Design and use of multiplexed chemostat arrays.

Authors:  Aaron W Miller; Corrie Befort; Emily O Kerr; Maitreya J Dunham
Journal:  J Vis Exp       Date:  2013-02-23       Impact factor: 1.355

10.  Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations.

Authors:  Gregory I Lang; Daniel P Rice; Mark J Hickman; Erica Sodergren; George M Weinstock; David Botstein; Michael M Desai
Journal:  Nature       Date:  2013-07-21       Impact factor: 49.962

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  1 in total

1.  Experimental evolution in morbidostat reveals converging genomic trajectories on the path to triclosan resistance.

Authors:  Semen A Leyn; Jaime E Zlamal; Oleg V Kurnasov; Xiaoqing Li; Marinela Elane; Lourdes Myjak; Mikolaj Godzik; Alban de Crecy; Fernando Garcia-Alcalde; Martin Ebeling; Andrei L Osterman
Journal:  Microb Genom       Date:  2021-05
  1 in total

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