A generalizable experimental framework for automated cell growth and laboratory evolution

Abstract

In the post-genomics era, exploration of phenotypic adaptation is limited by our ability to experimentally control selection conditions, including multi-variable and dynamic pressure regimes. While automated cell culture systems offer real-time monitoring and fine control over liquid cultures, they are difficult to scale to high-throughput, or require cumbersome redesign to meet diverse experimental requirements. Here we describe eVOLVER, a multipurpose, scalable DIY framework that can be easily configured to conduct a wide variety of growth fitness experiments at scale and cost. We demonstrate eVOLVER's versatility by configuring it for diverse growth and selection experiments that would be otherwise challenging for other systems. We conduct high-throughput evolution of yeast across different population density niches. We perform growth selection on a yeast knockout library under temporally varying temperature regimes. Finally, inspired by large-scale integration in electronics and microfluidics, we develop novel millifluidic multiplexing modules that enable complex fluidic routines including multiplexed media routing, cleaning, vial-to-vial transfers, and automated yeast mating. We propose eVOLVER to be a versatile design framework in which to study, characterize, and evolve biological systems.

Scalable continuous evolution of genes at mutation rates above genomic error thresholds

“…First, OrthoRep realizes continuous mutagenesis entirely in vivo . Therefore, it readily integrates with the existing rich ecosystem of yeast genetic selections including growth-based positive selections; dominant negative selections that may require titration of p1’s copy number ( Extended Data Figure 8 ); selections utilizing cell-based technologies such as fluorescence-activated cell sorting (FACS), continuous culturing devices, and droplet screening systems; and selections for cell-level phenotypes such as drug resistance, stress tolerance, or metabolism 1-3, 30 . To enable its widespread application, we have also established OrthoRep in different yeast backgrounds, including diploids and industrially relevant strains (manuscript in preparation).…”

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

“…propagating cultures many times a day, or splitting many independent cultures at different thresholds) as well as for dynamic alterations to passage protocols (e.g., alternating substrates or temperature from flask to flask) (Sandberg et al, 2017;Wong et al, 2018). Automation also allows for improved monitoring of physiological properties of evolving cultures, enabling researchers to better understand the evolutionary dynamics at play and to make informed decisions on when and how many times to sequence for adaptive mutations.…”

“…A number of automated ALE devices have been developed that span the range of culturing methods: large volume batch culture (Sandberg et al, 2014;LaCroix et al, 2014;Wong et al, 2018), microtiter plates (Horinouchi et al, 2014;Radek et al, 2017), chemostats/turbidostats (Blaby et al, 2012;Toprak et al, 2013), or microfluidics (Rotem et al, 2016). Such demonstrations establish the broad scope of technologies which can be part of automated strain construction workflows (Chao et al, 2017).…”

“…This ability to modify an ALE environment on the fly opens up a number of experimental possibilities -tolerance to certain chemicals can be incrementally built up, resulting in optimized bioproduction strains (Mohamed et al, 2017), or the growth environment can be rapidly alternated to yield strains with desirable diauxic phenotypes (Sandberg et al, 2017). The information needed to develop inexpensive, automated culturing systems is also becoming publicly available (Wong et al, 2018), contrasting with systems using industrial-grade hardware (LaCroix et al, 2014), and will doubtlessly lead to more widespread and innovative adoption of ALE as a powerful technique for both biological discovery and metabolic engineering.…”

Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes

“…These mutations can be further implemented in clean background strains, which can be followed by new iterations of the ALE experiment in an attempt to obtain additional beneficial mutations. Recent advances in automation technologies have allowed for automating all phases of the workflow, which increases the throughput of ALE experiments significantly compared to traditional labor-intensive approaches [ 14 ].…”