Patent Application
20210214713
Disclosed herein are novel methodologies for the experimental evolution of natural and synthetic microbes using a custom, high-throughput continuous culture system, and such continuous culture systems.
A number of “do-it-yourself” (DIY) continuous culture (CC) system designs have appeared in the literature in recent years (Toprak E., et. al., Nat. Genet. 44, 101-5 (2012); (Acar M., et. al., Nat. Genet. 40, 471-75 (2008); https://depts.washington.edu/soslab/turbidostat/pmwiki; http://openwetware.org/wiki/Turbidostat). A typical DIY CC system is comprised of integrated wetware, hardware, and software modules that can be readily interchanged and reconfigured. Maintenance of culture conditions (e.g., temperature, stirring speed, and media composition) can be automated over long periods (days to weeks). The modular customizability of DIY CC systems, and their ability to operate over long periods, enables the user to conduct a broad repertoire of evolution experiments. However, the configuration and application of DIY CC devices to areas of experimental evolution has remained under-explored.
Previously, most continuous culture (CC) evolution experiments were restricted to low throughput, limiting the ability to assess the relationship between culture evolvability and either culture conditions or parental strain genotype. Scalability of CC systems addresses this issue, as the effects of a variety of culture conditions on evolution can be tested simultaneously. Thus, CC systems can be used for a number of applications with potential commercial relevance, including testing new antibiotic treatment regimes for resistance acquisition or testing communities of strains for their ability to complement or support each other's growth.
As described herein, CC devices, in several different implementations, can serve as enabling technology for a number of under-explored areas in experimental evolution including selecting for novel functionality in a WT microorganism, improving engineered circuit stability by evolving both the circuit and host, testing and evolving stable multi-species communities, and testing engineered synthetic communities.
In some aspects, the disclosure relates to methods of performing experimental evolution on at least one fluidic microbial culture in a continuous culture system. In some embodiments, the method comprises: subjecting the at least one microbial culture to a dynamic environment, wherein the at least one microbial culture is exposed to a stress ramp function which is overlaid on top of a culture fitness function; and increasing the amount of stress applied to the at least one microbial culture in response to the increased fitness of the at least one microbial culture, wherein fitness is calculated in real-time.
In some embodiments, the fitness function ramp comprises more than one fitness measurement. In some embodiments, the fitness ramp function comprises a turbidity and/or a fluorescence measurement.
In some embodiments, the stress ramp function comprises more than one microbial stress. In some embodiments, the stress ramp function comprises an antibiotic, an antiseptic, a temperature, an aerobic, an anaerobic, an infectious, a nutrient, an irradiative, a pH, a metabolic, and/or a mechanical stress. In some embodiments, the stress ramp function comprises an increase or decrease in temperature.
In some embodiments, the at least one microbial culture evolves a novel functionality. In some embodiments, the novel functionality is selected from the list comprising stress tolerance, nutrient utilization, or metabolite production.
In some embodiments, at least one of the at least one fluidic microbial cultures comprises an archaea, a bacterium, a fungi, a protista, a microbial merger or symbiont, and/or a planarian. In some embodiments, at least one of the at least one fluidic microbial cultures comprises a suspension of mammalian cells, plant cells, or insect cells.
In some embodiments, the continuous culture system comprises integrated wetware, hardware, and software modules that can be readily interchanged and reconfigured. In some embodiments, the continuous culture system comprises a turbidostat with fluorescence detection. In some embodiments, the continuous culture system is configured to allow for vial-to-vial culture transfer. In some embodiments, the continuous culture system is configured to allow for continuous mixing of the microbial suspensions.
In some aspects, the invention relates to methods of testing the mutational stability of an engineered circuit. In some embodiments, the method comprises: subjecting a microbial cell comprising at least one engineered circuit to a dynamic environment, wherein the microbial cell is exposed to stress ramp function which is overlaid on top of a culture fitness function; increasing the amount of stress applied to the microbial cell in response to the increased fitness of the microbial cull, wherein fitness is calculated in real-time; and determining the time required for the engineered circuit to inactivate.
In some embodiments, the engineered circuit comprises a fluorescent output. In some embodiments, the fluorescent output is selected from the list consisting of TagBFP, mTagBFP2, Azurite, EBFP2, mKalama1, Sirius, Sapphire, T-Sapphire, ECFP, Cerulean, SCFP3A, mTurquoise, mTurquoise2, monomeric Midoriishi-Cyan, TagCFP, mTFP1, EGFP, Emerald, Superfolder GFP, Monomeric Azami Green, TagGFP2, mUKG, mWasabi, Clover, mNeonGreen, EYFP, Citrine, Venus, SYFP2, TagYFP, Monomeric Kusabira-Orange, mKOκ, mKO2, mOrange, mOrange2, mRaspberry, mCherry, mStrawberry, mTangerine, tdTomato, TagRFP, TagRFP-T, mApple, mRuby, mRuby2, mPlum, HcRed-Tandem, mKate2, mNeptune, NirFP, TagRFP657, IFP1.4, and iRFP.
In some embodiments, the fitness function ramp comprises more than one fitness measurement. In some embodiments, the fitness ramp function comprises a turbidity and/or fluorescence measurement.
In some embodiments, the stress ramp function comprises more than one microbial stress. In some embodiments, the stress ramp function comprises an antibiotic, an antiseptic, a temperature, an aerobic, an anaerobic, an infectious, a nutrient, an irradiative, a pH, a metabolic, and/or a mechanical stress. In some embodiments, the stress ramp function comprises an antibiotic. In some embodiments, the stress ramp function comprises an increase or decrease in temperature.
In some embodiments, the microbial cell is selected from the group consisting of an archaea, a bacterium, a fungi, a protista, a microbial merger or symbiont, and a planarian. In some embodiments, the microbial cell is selected from the group consisting of a mammalian cell, a plant cell, or an insect cell.
In some embodiments, the continuous culture system comprises integrated wetware, hardware, and software modules that can be readily interchanged and reconfigured. In some embodiments, the continuous culture system comprises a turbidostat with fluorescence detection. In some embodiments, the continuous culture system is configured to allow for vial-to-vial culture transfer. In some embodiments, the continuous culture system is configured to allow for continuous mixing of the microbial suspensions.
In some aspects, the invention relates to methods of testing the stability of at least one multi-species microbial community. In some embodiments, the method comprises: subjecting the at least one multi-species microbial community to a dynamic environment, wherein the at least one multi-species microbial community is exposed to a stress ramp function which is overlaid on top of a culture fitness function, increasing the amount of stress applied to the at least one multi-species microbial community in response to the increased fitness of the at least one multi-species microbial community, wherein fitness is calculated in real-time; and determining the fitness of each species independently.
In some embodiments, the fitness function ramp comprises more than one fitness measurement. In some embodiments, the fitness ramp function comprises a turbidity and/or a fluorescence measurement.
In some embodiments, the stress ramp function comprises more than one microbial stress. In some embodiments, the stress ramp function comprises an antibiotic, an antiseptic, a temperature, an aerobic, an anaerobic, an infectious, a nutrient, an irradiative, a pH, a metabolic, and/or a mechanical stress. In some embodiments, the stress ramp function comprises an antibiotic. In some embodiments, the stress ramp function comprises an increase or decrease in temperature.
In some embodiments, the multi-species microbial community comprises at least one of an archaea, a bacterium, a fungi, a protista, a microbial merger or symbiont, or a planarian. In some embodiments, the multi-species microbial community comprises at least one of a mammalian cell, a plant cell, or an insect cell.
In some embodiments, the continuous culture system comprises integrated wetware, hardware, and software modules that can be readily interchanged and reconfigured. In some embodiments, the continuous culture system comprises a turbidostat with fluorescence detection. In some embodiments, the continuous culture system is configured to allow for vial-to-vial culture transfer. In some embodiments, the continuous culture system is configured to allow for continuous mixing of the microbial suspensions.
In some aspects, the disclosure relates to methods of constructing a multi-species community comprising subjecting a multi-species community comprising microbial strains that comprise engineered circuits that facilitate cell-cell interactions to the method as described above.
In some aspects, the disclosure relates to continuous culture systems configured for high-throughput microbial evolution studies. In some embodiments, the continuous culture system configuration comprises at least one stress ramp function that is overlaid on top of at least one culture fitness function, wherein the relationship between the at least one stress ramp function and the at least one fitness function responds to increased culture fitness with increased application of stress in real-time.
In some embodiments, the continuous culture system comprises integrated wetware, hardware, and software modules that can be readily interchanged and reconfigured. In some embodiments, the continuous culture system is configured as a turbidostat with fluorescence detection to measure circuit output and track the loss or gain of circuit function over time. IN some embodiments the continuous culture system is configured to allow vial-to-vial culture transfer. In some embodiments, the continuous culture system is configured to allow continuous mixing of individual cultured species into a community culture. In some embodiments, the continuous culture system is configured for long-term continuous culture.
The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure, which can be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein. It is to be understood that the data illustrated in the drawings in no way limit the scope of the disclosure.
Others have reported the compositions of various DIY continuous culture (CC) systems (Toprak E., et. al., Nat. Genet. 44, 101-5(2012); (Acar M., et. al., Nat. Genet. 40, 471-75 (2008); https://depts.washington.edu/soslab/turbidostat/pmwiki; http://openwetware.org/wiki/Turbidostat). A typical DIY CC system is comprised of integrated wetware, hardware, and software modules that can be readily interchanged and reconfigured to perform a wide variety of long-term laboratory experiments. Maintenance of culture conditions (e.g., temperature, stirring speed, and media composition) can be automated over long periods. Such a system permits the user to collect multiple streams of data in real time, and algorithmically adjust culture conditions continuously.
DIY construction approaches can be easily scaled to high throughput. Moreover, CC systems are highly modular, and both device and software are relatively easy to reconfigure and operate. This modularity makes it possible for users to quickly elaborate on a system's capabilities as per the user's needs. In the past, most CC evolution experiments were restricted to low throughput, limiting the ability to assess the relationship between culture evolvability and either culture conditions or parental strain genotype. Scalability of DIY CC systems addresses this issue, as the effects of a variety of culture conditions on evolution can be tested simultaneously. Thus, DIY CC systems can be used for a number of applications with potential commercial relevance, including testing new antibiotic treatment regimes for resistance acquisition or testing communities of strains for their ability to complement or support each other's growth.
Biological organisms are embedded in complex, dynamically changing environments that shape their evolved phenotype (Elena S. F., et al., Nat. Rev. Genet. 4, 457-469 (2003); Nichols R. J., et al., Cell 144, 143-156 (2011); Nevozhay D., et al., PLoS Comput. Biol. 8, e1002480 (2012).) (
An intrinsic challenge in designing growth selection experiments lies in balancing the tradeoff between control and throughput (
Here we present eVOLVER, a multi-objective, DIY platform that gives users complete freedom to define the parameters of automated culture growth experiments (e.g. temperature, culture density, media composition, etc.), and inexpensively scale them to an arbitrary size. The system is constructed using highly modular, open-source wetware, hardware, electronics and web-based software that can be rapidly reconfigured for virtually any type of automated growth experiment. eVOLVER can continuously control and monitor up to hundreds of individual cultures, collecting, assessing, and storing experimental data in real-time, for experiments of arbitrary timescale. The system permits facile programming of algorithmic culture ‘routines’, whereby live feedbacks between the growing culture and the system couple the status of a culture (e.g. high optical density (OD)) to its automated manipulation (e.g. dilution with fresh media). By combining this programmability with arbitrary throughput scaling, the system can be used for fine resolution exploration of fitness landscapes, or determination of phenotypic distribution along multidimensional environmental selection gradients.
We demonstrate the broad applicability of eVOLVER by configuring it to perform diverse growth and selection experiments. First, we conduct high-throughput experimental evolution on yeast populations using multi-dimensional selection criteria, scanning culture density space at fine resolution to assess adaptive outcomes. Next, by performing growth selection on a yeast knockout (YKO) library under temporally variable temperature stress regimes, we show that eVOLVER can be used to systematically explore the relationship between environmental fluctuations and adaptive phenotype. Finally, by integrating millifluidic multiplexing modules, we demonstrate that eVOLVER can carry out complex fluidic manipulations, dramatically extending the scope and range of possible growth and selection experiments.
As described herein, CC devices, in several different implementations, can serve as enabling technology for a number of under-explored areas in experimental evolution including selecting for novel functionality in wild-type microorganisms, improving engineered circuit stability by evolving both the circuit and host, testing and evolving stable multi-species communities, and testing engineered synthetic communities.
In some aspects, the disclosure relates to a continuous culture system that is configured for high-throughput culturing of microorganisms. As used herein the terms “microbial,” “microbe,” or “microorganism” all relate to microscopic living organisms including archaea, bacteria, fungi, protista, microbial mergers or symbionts, planarians (e.g., C. elegans), and suspensions of mammalian cells, plant cells, or insect cells.
In some aspects, the disclosure relates to a continuous culture system that is configured for high-throughput microbial evolution studies. Batch culture experiments are traditionally used for high-throughput laboratory evolution studies. Configuring a CC system to perform experiments at high-throughput offers a way to do continuous culture experiments at the same scale. While more technically challenging than performing batch culture, a continuous culture system allows for precise control and monitoring of growth conditions during an evolution experiment. By virtue of its scalability, a CC system permits the systematic exploration of the relationship between culture regime and adaptation. Antibiotic resistance experiments are one example of an application for this approach, as a CC system configured for high-throughput can be used to test the relationship between antibiotic resistance and different quantitative features of the stress ramp algorithm.
In some embodiments, a continuous culture system configuration comprises at least one stress ramp function that is overlaid on top of at least one culture fitness function, wherein the relationship between the at least one stress ramp function and the at least one fitness function responds to increased culture fitness with increased application of stress in real-time. As used herein, a “culture fitness function” refers to an output that is indicative of microbial growth or health. In some embodiments, the culture fitness function consists of one microbial fitness measurement. In other embodiments, the culture function comprises more than one fitness measurement. Examples of fitness measurements are provided below (e.g., turbidity and fluorescence). As used herein, the term “stress ramp function” refers to an input that applies stress on microbial growth or health. In some embodiments, the stress ramp function consists of one microbial stress. In other embodiments, the stress ramp function comprises more than one microbial stress. Examples of microbial stresses are provided below.
In some aspects, the disclosure relates to a continuous culture system that is configured for the testing of mutational stability of engineered circuit variants (e.g., assaying how long it takes for a circuit to inactivate or lose at least some portion of its function). A major focus of synthetic biology has been on engineering synthetic regulatory circuits to enable user-defined control of cellular function. Circuits engineered in E. coli, yeast, and other microorganisms often impose a fitness burden on their host cells and may be lost or mutated over time. Very little work has gone into engineering circuits that are either robust to mutation or minimize host-cell burden. By the same token, efforts to engineer strains that can accommodate circuits without mutating them have not been undertaken.
In some aspects, the disclosure relates to a continuous culture system that is configured to assay circuit stability by growing at least one microbial cell comprising at least one circuit (or circuit library) and then assessing mutations that accrue to either the at least one circuit or the genome of the host microbial cell. In some embodiments, the at least one microbial cell comprising at least one circuit (or circuit library) is grown under stress. By selecting for circuit variants or strain backgrounds that render the circuit resistant to inactivation or loss of function, engineering rules for circuit stability can be determined.
In some embodiments, the continuous culture system is configured as a turbidostat with fluorescence detection to measure circuit output and to track the loss (or gain) of circuit function over the course of an experiment. As used herein. “turbidostat” refers to a culture device that has feedback between the turbidity of the culture vessel and the dilution rate. In some embodiments, the circuit output is a detectable marker, such as the production of a fluorescent protein including, but not limited to, TagBFP, mTagBFP2, Azurite, EBFP2, mKalama1, Sirius, Sapphire, T-Sapphire, ECFP, Cerulean, SCFP3A, mTurquoise, mTurquoise2, monomeric Midoriishi-Cyan, TagCFP, mTFP1, EGFP, Emerald, Superfolder GFP, Monomeric Azami Green, TagGFP2, mUKG, mWasabi, Clover, mNeonGreen, EYFP, Citrine, Venus, SYFP2, TagYFP, Monomeric Kusabira-Orange, mKOκ, mKO2, mOrange, mOrange2, mRaspberry, mCherry, mStrawberry, mTangerine, tdTomato, TagRFP, TagRFP-T, mApple, mRuby, mRuby2, mPlum, HcRed-Tandem, mKate2, mNeptune, NirFP, TagRFP657, IFP1.4, or iRFP. In some embodiments, the circuit output is the production of a protein tagged with a fluorescent protein.
In some aspects, the disclosure relates to a continuous culture system that is configured to test and evolve stable multi-species communities. There is a growing research interest in understanding how microbial interspecies interactions give rise to stable communities. A properly configured CC system can be used to test the stability of community interactions in a long-term continuous growth setting. At high throughput, different combinations of species and variations in culture conditions can be tested for their ability to support community fitness and structure. In some embodiments, a CC system is configured as a turbidostat and used to culture species combinations in order to assess interaction stability and emergent community structure. In some embodiments, the fluidics of a CC system are configured to allow vial-to-vial culture transfer. In some embodiments, the continuous culture system enables the continuous mixing of individual cultured species into a community culture. As used herein, a “multi-species community” is a culture in which at least two independent microbe species co-exist. In some embodiments, the at least two independent microbe species are from the same kingdom, phylum, class, order, or genus. In some embodiments, the at least two independent microbe species are from a different kingdom, phylum, class, order, or genus.
In some aspects, the disclosure relates to a continuous culture system that is configured to engineer synthetic communities. In some embodiments, a continuous culture system is configured as a tool to facilitate the bottom-up construction of microbial communities from strains harboring engineered circuits that facilitate cell-cell interaction. In some embodiments, a continuous culture system is configured to test the stability of interactions between strains. In other embodiments, a continuous culture system is configured to generate continuous co-cultures through vial-to-vial transfer.
In some embodiments, the continuous culture system comprises integrated wetware (e.g., culture vessels and fluidics), hardware (e.g., electronics), and software (e.g., Python, Arduino C and Javascript) modules that can be readily interchanged and reconfigured. Some embodiments of the wetware, hardware and software modules are described in more detail elsewhere herein.
In some embodiments, the continuous culture system is configured for long-term continuous culture. As used herein, “long-term” refers to any prolonged period of time, including, but not limited to, one or more hours, one or more days, one or more weeks, one or more months, or one or more years.
In other aspects, the disclosure relates to a method of performing experimental evolution on at least one fluidic microbial culture in a continuous culture system. In some embodiments, at least one microbial culture is subjected to a dynamic environment, wherein the at least one microbial culture is exposed to a stress ramp function which is overlaid on top of a culture fitness function, and increasing the amount of stress applied to the at least one microbial culture in response to the increased fitness of the at least one microbial culture. In some embodiments, microbial fitness is calculated in real-time.
In some embodiments, microbial fitness is calculated through turbidity or fluorescence measurements.
In some embodiments, the stress applied to the at least one microbial culture is an antibiotic. In some embodiments, the stress applied to the at least one microbial culture is an antiseptic, including but not limited to, alcohol (e.g., ethanol), hydrogen peroxide, iodine, benzalkonium chloride, or boric acid. In other embodiments, the stress applied to the at least one microbial culture is an increase or decrease in temperature. In other embodiments, the stress applied to the at least one microbial culture is selected from the list comprising: growth under redox stress, growth under aerobic or anaerobic conditions, or growth under challenge from an infectious agent (e.g., a fungi, bacteria, phage or virus). In some embodiments, the stress applied to the at least one microbial culture is a nutrient stress. In some embodiments, the nutrient stress is a nutrient poor condition (i.e., the nutrient condition is insufficient to meet the microorganism's bioenergetics needs). In some embodiments, the nutrient stress is a nutrient rich condition (i.e., the nutrient condition exceeds the microorganism's bioenergetics needs). In some embodiments, the stress is irradiation. Examples of ionizing and non-ionizing irradiation are known to those having skill in the art and include, but are not limited to, alpha particles, beta particles, positrons, photons, charged nuclei, neutrons, gamma rays, X-rays, UV, infrared, microwaves, radio waves, or cosmic rays. In some embodiments, the stress is a change in pH. In some embodiments, the stress a metabolic stress (e.g., anabolic or catabolic). In some embodiments, the metabolic stress is an increase or decrease in gene expression or protein translation relative to the level of gene expression or protein translation in the absence of the metabolic stress. In some embodiments, the stress is a mechanical stress (e.g., increased or decreases in pressure, increased or decreases in vibration, increase or decreases in motion, etc.). In other embodiments, more than one stress is applied to the at least one microbial culture.
In some embodiments, at least one microbial culture evolves a novel functionality. In some embodiments, the novel functionality is altered stress tolerance, altered nutrient utilization, or altered metabolite production, in which “altered” means changed relative to the starting microbial culture. Stress tolerance includes tolerance to higher or lower temperatures, higher or lower concentrations of salts in the culture, higher or lower amounts of sugars or other carbon-based feedstocks in the culture, etc. Altered nutrient utilization includes altered rates (lower or higher) of nutrient utilization and/or utilization of different or additional nutrients. Altered metabolite production includes altered rates (lower or higher) of metabolite production and/or production of different or additional metabolites. In other aspects, the disclosure relates to a method of performing experimental evolution.
In some aspects, the disclosure relates to a method of culturing of microorganisms.
In some aspects, the disclosure relates to a method of testing the mutational stability of a microbial cell that comprises an engineered circuit. In some embodiments, the method relates to culturing at least one fluidic microbial culture in a continuous culture system and determining the time required for an engineered circuit to inactivate after subjecting a microbial cell to a dynamic environment, wherein the at least one microbial culture is exposed to a stress ramp function which is overlaid on top of a culture fitness function, and increasing the amount of stress applied to the at least one microbial culture in response to the increased fitness of the at least one microbial culture. As used herein, the term “inactivate” refers to a decrease in the output of an engineered circuit by at least about 20%, 25%, 30%, 40%. 50%, 60%, 70%, 75%. 80%, 90%, 95%. 99% or more than 99% relative to the output prior to application of the stress.
In some embodiments, microbial fitness is calculated in real-time. In some embodiments, the method evolves both the circuit and the microbial host cell. Engineered circuits, such as engineered gene circuits for expressing one or more outputs (such as proteins) in response to one or more signals, are known in the art.
In some aspects, the disclosure relates to a method of testing the stability of at least one multi-species microbial community. In some embodiments, the method relates to culturing at least one fluidic multi-species microbial culture in a continuous culture system and determining the fitness of each species independently after subjecting a microbial cell to a dynamic environment, wherein the at least one microbial culture is exposed to a stress ramp function which is overlaid on top of a culture fitness function, and increasing the amount of stress applied to the at least one microbial culture in response to the increased fitness of the at least one microbial culture. In some embodiments, microbial fitness is calculated in real-time.
In other aspects, the disclosure relates to a method of constructing a multi-species community. In some embodiments, the method relates to culturing a multi-species microbial community in a continuous culture system, wherein the multi-species community comprises microbial strains that comprise engineered circuits that facilitate cell-cell interactions. A multi-species microbial community can include two or more microbial strains (of which none, some or all may include engineered circuits), such as 2, 3, 4, 5, 6, 7, 8, 9, 10, or more microbial strains. In some embodiments, the multi-species microbial community is subjected to a dynamic environment, wherein the microbial community is exposed to a stress ramp function which is overlaid on top of a culture fitness function, and increasing the amount of stress applied to the microbial community in response to outputs from the engineered circuits. In some embodiments, the output is calculated in real-time.
A typical scheme for such a system is depicted in
Media input and efflux from each culture can be maintained by an array of peristaltic pumps. CC systems often feature an electronics control layer—composed of commercially available data acquisition boards, microcontrollers (e.g., Arduino), and switch arrays—is used to control all fluidic and sleeve-associated functions. Microcontrollers and relays may be connected by USB to a laptop computer running control software. The software can be configured to specify control sleeve and fluidic settings for each individual culture. The laptop may be attached to a server that collects and stores experimental data. Plotted data can be streamed to a webpage allowing experiments to be observed remotely by either laptop mobile device.
The modular customizability most DIY CC systems enables the user to conduct a broad repertoire of evolution experiments. For example, in a simple implementation, chemostat experiments can be run by programming the fluidics module for steady-state media influx and efflux. When integrated with data collection form a control sleeve-mounted led/detector pair, a device can be used to perform turbidostat experiments. Depending on the type of experiment, a system can be configured to collect data from additional electronic component sources mounted on the control sleeve. For example, in addition to temperature and OD reading associated with a standard turbidostat, a light source and detector can be installed to collect fluorescence data if a user was interested in monitoring gene expression.
By integrating real-time data collection and processing with fluidic function, it is possible to specify dynamic culture environments with contingency-based stress regimes. For example, a stress ramp function can be overlaid on top of turbidostat function by including an additional set of input pumps to supply media containing a stress agent (e.g., antibiotics), and then gradually increasing the ratio of stress/no stress media added to the cultures over the course of the experiments. In
The introduction of engineered circuits into cells can result in a growth penalty through 1) placing a substantial metabolic load on the system or 2) misregulation of native function. Very little research has gone into understanding how to engineer circuits which do not impose a fitness burden. Here, a CC platform was used to test the stability of simple two-node circuits in yeast. The circuits consist of an inducible promoter driving expression of a transcriptional activator that, in turn, activates expression of a GFP reporter gene (
In order to insulate the circuit from this interaction, several aspects of the activator-promoter interaction were engineered at the reporter node. The ability of the insulated circuit to ameliorate the fitness defect was tested by growing each of the strains in media containing inducer, in a CC system configured to run as a turbidostat. As shown in
Described here in detail is the continuous culture system that has been constructed to execute various experiments. The described implementation is specifically designed for doing pressure step turbidostat experiments that feature two media input lines, one efflux line, and vial-proximate instrumentation that monitors culture OD at a fixed stirring rate and temperature. Elaboration or rearrangement of the components to realize other experimental setups can be accomplished easily.
Overall Design:
The continuous culture system design features separate modular wetware (culture vessels and fluidics), hardware (DIY electronics) and software (Python, Arduino C and Javascript) layers that can be adjusted or upgraded individually. For example, the custom electronic layer controls each experimental dimension (e.g., temperature, fluidics, etc.) with separate Arduino microcontrollers. The design allows for upgrades that are made within each control module to not require restructuring of the entire platform. This allows for quick customization if new experimental capabilities are desired. Additionally, using Arduino microcontroller boards enable users to test their own customized sensors before large-scale integration into the full platform.
Wetware:
Evolution experiments are conducted in 40 mL glass vials fitted with custom machined Teflon tops. Segments of PEEK tubing are inserted into the tops and connect silicone tubing lines to the vials. Silicone tubing is attached to peristaltic pumps that push media into the vials from large volume (5 or 10 L) reservoirs, or pull culture out of vials and into a waste reservoir. Prior to running experiments, vials, media reservoirs, and lines are assembled and sterilized, and then connected together before loading media into the vials array.
Hardware:
Smart Sleeve:
During experiments, culture vials are housed in easily manufactured smart sleeve units. The units consist of 3D printed chassis which are used to mount an aluminum jacket for controlling temperature, and an inexpensive computer fan with attached magnets in order to spin stir bars inside culture vials. Each smart sleeve features a small circuit board that serves as a mount for connecting electronic components. Heating of the aluminum jacket is accomplished by heating resistors that directly contact the aluminum tubes, while temperature is measured with inexpensive thermistors. Optical density is measured using an inexpensive LED photodiode emitter/detector pair. Additional smart sleeve customizations include sensors that measure levels of dissolved O2, CO2, N2, or other gases; sensors that measure redox potential; and/or emitters/detectors that excite and detect multiple wavelengths of light for optogenetics or fluorescence-based assays. The mounted boards gather signals for all components on a single sleeve into a ribbon cable that leads to a custom PCB motherboard.
Electronics:
The function of the motherboard is to take sets of signals from the smart sleeve array and sort them to the appropriate control and sensor boards. This setup enables plug and play of corresponding smart sleeve and board functions, with a single motherboard designed to route up to seven sensors/actuators to an array of 16-smart sleeves. Cable ribbons that correspond to each function run from the motherboard to each control board. The printed circuit boards were obtained from International Circuits Inc. (internationalcircuits.com).
Custom Arduino shields were designed for either reading and supplying voltages for several of the smart sleeve components. For reading analog signals, a simple voltage divider circuit board was constructed that connects to an Arduino Mega. While this board design is amenable to reading a variety of analog voltages, in this specific system's implementation the boards were used to obtain readings from the thermistors. This board can accommodate up to 16 channels and uses a 9 V power supply. To best make thermistor measurements, a 10 Ohm resistor was used, and no additional buffer or filter was required. This design is modular, and could be adapted to gather voltage data from other types of components. For OD measurements, two 8 channel National Instruments digital acquisition (DAQ) devices were used, which were connected to the motherboard via a ribbon cable and breakout board.
To regulate the power supplied to components, a shield for the Arduino Uno was designed that is able to perform PWM voltage control. This transistor control board uses TLC5940 PWM expansion chip to control an N-Channel MOSFET transistor array. One board can supply addressable voltage of up to 60 V to a single set of components for a 16-unit smart sleeve array. Thus, in this present implementation three of these boards are used to supply power for stirring, heating, and LEDs (powered at 9V, 12V and 9V respectively).
Pump Control:
Switch arrays boards made by Sainsmart (sainsmart.com/relay-1/relays/sainsmart-imatic-16-channels-wifi-network-i-o-controller-kit-for-arduino-relay-android-ios.html) were used to control small inexpensive peristaltic pumps. This particular device implementation has 3 pumps per smart sleeve, for a total of 48 pumps in a single array. This allowed for devoting two pumps to input (one media and the other media+stress) and one to efflux. Switch arrays are controlled by an Arduino Mega connected to a 7.5 V DC power supply. Pump and smart sleeves are arranged as a tower, with the smart sleeve layer on top, and each of the three pump layers stacked below it.
Software:
All microcontrollers are connected to a generic PC laptop via USB connections. The system is controlled using custom code written in Python and Arduino C. The Python code consists of three long-running processes, responsible for data acquisition, data analysis, and pump driving. Data from either the DAQ device or the analog input circuit are processed and saved. Upon reaching the OD threshold, pump driving times are calculated and sent to the pump driving process. Data are sent through pipes, by connecting the output of one process to the input of another. Data is stored on the lab computer in the HDF5 format, a commonly-used file format for storing data in the scientific community.
The Arduino C code controls the stirring and temperature of the vials. Due to the high speed of the stirrers at even low voltages, pulse-width modulation is performed by in the Arduino software to achieve appropriate spin rates. Temperature control is performed by measuring the resistance of a thermistor (which changes with temperature) flush against the aluminum sleeve, and adjusting the current to the heating resistors using a proportional-integral-derivative controller written in Arduino C.
Data visualization software is written in HTML and JavaScript (JS), with data displayed using elements of from the Dygraphs (dygraphs.com) library. The HTML consists of a barebones website containing empty div Lags for JS to change dynamically. The JS code is responsible for setting up event handlers (e.g. menu clicks, zooms and drags), setting up Dygraph objects for displaying the data, and communicating with a Python Tornado server running on the lab computer over the WebSockets protocol for sending and retrieving data. This arrangement allows multiple users to view plotted data remotely by logging into a secure webpage onto which data being collected are dynamically streamed.
Strain Construction:
Genotypes for yeast and bacterial strains used in this study are listed in TABLE 2. Plasmids were constructed using standard molecular biology techniques. Strains were generated using standard lithium acetate transformation. When appropriate, non-isogenic pooled population samples were given unique designations for clarity (TABLE 2).
S. cerevisiae
S. cerevisiae
S. cerevisiae
Escherichia
coli K-12
Escherichia
coli K-12
Serratia
marcescens
S. cerevisiae
S. cerevisiae
Routine Cell Culture Techniques:
Culture conditions varied according to the needs of particular experiments. Cultures used to seed eVOLVER experiments were prepared as follows: Saccharomyces cerevisiae (obtained from frozen stock or single colonies) was grown in 2 mL of YPD media (2% glucose) at 30° C. in a shaking incubator (300 rpm) for at least 36 h. For routine overnight culture of Escherichia coli or Serratia marcescens, cells obtained from frozen stock were used to inoculate 2 mL of LB Miller broth grown at 37° C. for 12 h in a shaking incubator (300 rpm).
Flow Cytometry:
Flow cytometry was used to measure single-cell fluorescence throughout the study. Prior to measurement, 200 uL of yeast culture (see additional methods for experiment-specific growth conditions) was diluted with 100 uL of filter-sterilized PBS supplemented with cyclohexamide to a final concentration of 20 ug/mL, then incubated at 4° C. in the dark for no less than 3 h to allow for fluorophore maturation. An Attune NxT Flow Cytometer (Invitrogen) equipped with an autosampler was used to acquire data. For a typical experiment, at least 10.000 events were acquired. Cells were analyzed using FlowJo (Treestar Software). Intact cells were gated using forward and side scatter, followed by gating on fluorescence channels (green and/or red, as appropriate) to determine the fractional distribution of each population.
Fitness Calculations:
Competitive fitness, in which a strain of interest is co-cultured in competition with a reference strain, was assayed in the same fashion throughout the study. The ratio of the two strains was determined at multiple timepoints—generally at the beginning and end of an experiment—either by flow cytometry or qPCR. Fitness values, F, were calculated using the following equation (Kryazhimskiy S., et al., Science 344, 1519-1522 (2014)):
where t is number of generations, and n and n, are cell counts for the strains of interest and reference strain, respectively.
Setup Procedure for eVOLVER Experiments:
Prior to each eVOLVER experiment, 40 mL borosilicate glass vials outfitted with a stir bar and capped with an influx port and an efflux port were sterilized by autoclave. Media and waste lines were sterilized by pumping 10% bleach (20 mL), followed by 70% ethanol (20 mL). Lines were cleared by pumping air for 20 s, followed by media (20 mL). Media and waste lines were then attached to the influx and efflux ports of each vial, and each vial filled by pumping 25 mL of the appropriate media through the influx port. At this point, Python control code was initiated, triggering a blank media measurement, and activating Smart sleeve heating elements. Prior to seeding with cells, fresh media was incubated in the device for a length of time sufficient for the first 15 optical density recordings to be taken (˜2.5 min) and for media to reach the programmed temperature.
Ertracting Yeast Genomic DNA:
To extract genomic DNA, ˜2×106 yeast cells (roughly 30 μL of overnight culture) were pelleted by centrifugation (5 min, 1000 rcf). Supernatant was removed, and pellets were resuspended in 30 μL 0.2% sodium dodecyl sulfate (SDS), followed by vortexing for 15 s. Suspensions were transferred to PCR tubes and heated in a thermal cycler (37° C.) for 5 min, followed by 98° C. for 5 min before cooling to 4° C. Extracts were diluted with H2O to a final volume of 75 p L prior to being used as PCR template. Primers used in this study are listed in TABLE 2.
Library Preparation and Barcode Sequencing:
Libraries for next-generation sequencing were prepared by PCR-amplification of genomic DNA (LightCycler 480 Instrument II, Roche), purification (Zymo Research). Primers used for indexing (culture, timepoint) and sequencing adapters were added via PCR. NextSeq sequencing (Harvard Biopolymers Facility) was used to sequence the culture index, the timepoint index, and a 55 bp single end read of the barcode construct. PhiX was added at 50% to increase sequence diversity. Alignment was performed using custom code harnessing MATLAB's Bioinformatics Toolbox and Boston University's parallel computing cluster. Alignment scores were calculated using the Smith-Waterman algorithm (swalign function) and assigned based on best score above a minimum threshold. In total, we assigned more than 244 million reads to 5149 unique library members to track frequency across the 4 time points for each of the 16 conditions.
Characterizing eVOLVER Operation and Stability with Long-Term Bacterial Growth:
To demonstrate a typical use case of eVOLVER and simultaneously characterize the operation and stability of the platform, we conducted a long-term E. coli growth experiment in turbidostat mode using a basic setup with peristaltic pumps for fluidic handling. Throughout the experiment, we monitored contamination, calibration settings, and stability of both software and hardware.
An overnight culture of E. coli MG1655 was grown in pre-buffered LB Miller media (pH 7.2). This culture was used to inoculate 25 mL of M9 media containing 0.4% glucose and 1 ug/L thiamine (hereafter referred to as M9), and this was grown to mid exponential phase. Meanwhile, 16 eVOLVER vials were initialized with antibiotic-free M9 media. Eight vials were inoculated with cells from the mid-exponential culture, interspersed with eight vials that were left uninoculated. The cultures, maintained at 37° C. in turbidostat mode (OD window=0.15-0.2), exhibited similar growth rates over the first 48 hours as expected (
To limit selection for biofilm, manual daily transfers were commenced after 72 hours of growth, when biofilm accumulation was initially observed. Sequentially, vials were removed from their Smart Sleeves, 2 mL of culture was diluted into a freshly autoclaved vial containing 23 mL fresh media, which were then inserted into the vacant Smart Sleeve. Transferring all eight cultures took approximately 10 minutes, and cultures did not appear to be negatively affected by these periodic transfers. The cultures were maintained for a total of 250 hours (1.5 weeks), constituting over 200 generations in exponential growth.
During this time, we observed no unplanned software or hardware failures, consistent with our other long-term culture experiments. Data collection was momentarily paused during routine vial examination and transfers, which constituted an expected downtime of less than 1% of experimental time (
At the conclusion of the experiment, temperature, OD, and fluidics were all re-calibrated, and their calibration values compared to those from the start of the experiment (
Conducting Experimental Evolution Across a Multidimensional Selection Space:
In order to showcase eVOLVER's ability to conduct long-term, continuous culture laboratory evolution experiments at high throughput, we explored the relationship between culture density and fitness in evolving yeast populations. To accomplish this, we configured the eVOLVER to function as a turbidostat, where culture density is continuously maintained within a constant, defined density window by an automated, OD-dependent media dilution routine. By varying the upper and lower thresholds that define each density window, we scanned a multi-dimensional space of population density at fine resolution to assess adaptive outcomes.
Parallel Evolution Across Density Space:
A single colony of prototrophic S. cerevisiae FL100 (ATCC 28383) was pre-adapted in eVOLVER continuous culture (turbidostat mode. OD 0.25-0.3) in Synthetic Complete (SC) medium+2% glucose (Sunrise Science Products) for 100 generations. A single colony from this population was selected as the founder strain (yBW001) for the high throughput, density-dependent evolution (
Culture densities were monitored continuously, permitting calculation of population size, mean doubling time (or growth rate), and genome replication events in each vial during the course of the experiment (
At the limiting glucose concentration we used, cultures exhibit a reduced carrying capacity and observable metabolic or diauxic shifts. Consequently, by simply setting the upper and lower density thresholds of the culture with eVOLVER, we could observe an impact on the resulting metabolic niche. For example, if the density window is below the diauxic point, the characteristic shift is never observed; conversely, if the window is high, the population exhibits two distinct phases of growth. The duration in each phase and the number of shifts seen per generation of growth varies across the sampled landscape.
Competitive Fitness in Different Density Niches:
At the conclusion of 500 h of continuous growth, each culture was struck out on a YPD plate. Three colonies were picked from each plate, grown in SC+0.06% glucose, and frozen in glycerol. These stocks were used to seed competitive fitness assays against the fluorescently labeled founder strain (yBW002) (
Characterization of Evolutionary Parameters on Niche Fitness:
Next, we wanted to determine if resulting fitness measurements were significantly correlated with any unique environmental parameter during evolution. High and low density fitness measurements for each colony were plotted on the same scatter plot and clustered using k-means, yielding three distinct groups: low-density specialists, high-density specialists, and colonies with low fitness in both measured niches (
Growth Selection Under Temporally Varying Selection Regimes:
There is growing interest in interrogating biological systems in fluctuating conditions that more closely reflect the dynamics of natural environments (Berry D. B., et al., PLoS Genet. 7, e1002353 (2011); Gibney P. A., et al., Proc. Natl. Acad. Sci. 110, E4393-E4402 (2013)). In fluctuating environments, different phenotypes and adaptations may arise than in environments with monotonic selection pressure. eVOLVER makes it possible systematically study the relationship between temporal fluctuation and phenotypic selection, while holding other environmental variables constant. To demonstrate this, we performed growth selection experiments on a pooled yeast knockout library (Giaever G., et al., Nature 418, 387-391 (2002); Breslow D. K., et al., Nat. Methods 5, 711-718 (2008); Li Z., et al., Nat. Biotechnol. 29, 361-7 (2011); Giaever G. and Nislow C., Genetics 197, 451-465 (2014)), under conditions in which a single environmental variable—temperature—was temporally varied.
Dynamic Temperature Selection on Yeast Knockout Library:
A 500 uL aliquot of the pooled haploid MATα yeast knockout collection (Transomic TKY3502P) was thawed and grown in 500 mL YPD under constant shaking (300 rpm) at 30° C. for 12 h. Cells were then seeded in eVOLVER vials (containing YPD+50 ug/mL carbenicillin+25 ug/mL chloramphenicol) at an initial OD 0.05. Cultures were grown at 30° C. for 5.5 h in order to reach OD ˜0.15, and then maintained in continuous turbidostat culture (OD 0.15-0.20) over the course of the experiment. Temperature perturbations of varying magnitude and period (
Growth rate was clearly observed to vary in response to temperature changes (
Two mL culture samples were taken every 48 h for six days and frozen at −80° C. For actively growing cultures, regular automated dilution events were sufficient to replace culture volume lost by sampling. For the 42° C./step condition, in which no dilution events were triggered because of a lack of growth, fresh YPD was manually added to replace culture volume after each sample. Genomic DNA was extracted from each of the 64 samples (16 temperature profiles×4 timepoints) by thawing at room temperature, pelleting cells at 1000 rcf for 5 min, and then performing a genomic extraction protocol.
Library Preparation and Barcode Sequencing:
Library preparation was performed in two stages (
In the second stage, indexes and sequencing adapters were added for every timepoint-vial combination, using a small number of cycles to minimize amplification. Amplification with i5-indexed primers prCM363-366 paired with i7-indexed primers prCM373-388 (TABLE 3) was performed in a 50 uL reaction using the following cycle conditions: (i) denaturation: 95° C. for 10 min; (ii) extension (5 cycles): 95° C. for 10 s, 65° C. for 10 s, 72° C. for 20 s; (iii) amplification (7 cycles): 95° C. for 10 s, 72° C. for 20 s; (iv) elongation: 72° C. for 7 min. Resulting DNA was again purified using a DNA Clean Concentrator Kit. DNA concentrations were determined using a Nanodrop Onec Spectrophotometer, and were mixed in equimolar amounts to form the final indexed library pool. The pool was diluted to 1 ng/uL and submitted to the Biopolymers Facility (Harvard Medical School). NextSeq sequencing was used to sequence the 8 bp i5 index, the 8 bp i7 index, and a 55 bp single end read of the barcode construct. Due to shared sequences in the regions flanking the barcode. PhiX was spiked in at 50% to increase sequencing diversity.
Sequencing Alignment and Frequency Computations:
Alignment was performed using custom code harnessing MATLAB's Bioinformatics Toolbox and Boston University's parallel computing cluster. Reads were tabulated for each vial and timepoint using the index sequences (
Population frequency of each library member was calculated by dividing the number of reads assigned to each member by the total number of assigned reads for a given indexed sample (
It should also be noted that while the sequencing depth is sufficient for the evenly distributed samples at Day 0, the wide range of frequencies observed following enrichment would be better measured at higher sequencing depth with reduced multiplexing of samples. As a result, we were careful not to draw strong conclusions from library members present at low frequencies in downstream analysis.
Cross-Correlation Between Temperature Conditions:
By prescribing experimental parameters in a programmable fashion, eVOLVER allows us to scan through environmental spaces. This allows us to identify similarities and differences in how these environments affect fitness outcomes, drawing conclusions about the environments themselves. For this experiment, we used the library performance data in order to delineate regions in temperature magnitude-frequency space that exert similar selection pressures on cells.
As a first pass, we looked at the correlation in 100 high-performing members from each condition. High-performing members were defined as those with the largest arithmetic difference in frequency between initial and final timepoints. When the overlap between these high-performing strains is quantified by tabulating the number of members shared between each condition (a simplified cross-correlation metric), relationships in the temperature magnitude-frequency space are revealed (
When comparing the degree of similarity between a particular condition and each other condition, two regions of interest emerge. One region comprised of the conditions of low/moderate temperature magnitude and moderate/high temperature frequency exhibits a large degree of overlap, suggesting these conditions exert similar selective pressures on cells. In contrast, the conditions on the periphery, particularly those with high temperature magnitude, exhibit minimal overlap with other conditions, suggesting that unique selection pressures are at work. This approach is limiting however, as the definition of “high-performing members” biases which members are considered, rather than considering the entire dataset.
To further examine the similarities and differences between conditions, principle component analysis was applied. First, the arithmetic difference in frequency between the initial and final timepoints was calculated for each library member, tabulating the results in a vector for each condition. Library members that were missing at the Day 0 timepoint were excluded from analysis. The data from the 36° C./step and 42° C./step conditions were excluded from further analysis due to insufficient read depth. The frequency difference vectors were used to construct a 14×14 cross-correlation matrix to quantify the similarity between conditions. Principle component analysis was applied to the resulting cross-correlation matrix to separate the conditions across two axes (
We observed three clusters in PCA coordinate space that are relatively stable over time: one large group that clusters with the mild temperatures, and two small clusters, corresponding to high temperature/high frequency, and high temperature/low frequency conditions. The library was divided into subsets with shared Saccharomyces Genome Database (Cherry J. M., et al., Nucleic Acids Res. 40, D700-D705 (2012)) (SGD) annotations of gene ontology (GO) Welch's t-statistic was applied to determine whether these GO terms are linked to significant changes in fitness for the conditions which comprise each PCA cluster. Correcting for multiple hypotheses, we found several cellular functions to significantly affect fitness in one or more of these PCA clusters (
Fitness Centroid Calculations:
Using eVOLVER to scan along different experimental parameters results in multidimensional fitness data spanning an environmental space, which can be challenging to visualize and interpret. In order to aid in analysis and visualization, we chose to transform the fitness of library members into temperature magnitude-frequency space. To do this, we computed a weighted fitness centroid, compiling the fitness in each condition into a pair of coordinates in temperature magnitude-frequency space.
Mean fitness of each library member in a particular condition can be calculated over different time periods using the population frequency in place of a ratio between two strains. Here the fitness computed over the Day 0-Day 6 range was used for all downstream analysis. Fitness centroids for each library member were calculated by averaging the coordinates of each condition in temperature magnitude-frequency space, with the fitness in each condition serving as weights. In this manner, library members with differential performance across conditions would exhibit shifted fitness centroids towards conditions in temperature magnitude-frequency space in which they were more fit (
To visualize the dataset of fitness centroid calculations, the centroid from each library member was plotted in a single scatter plot along the axes of temperature magnitude and temperature frequency (
The fitness centroid approach has both advantages and disadvantages. The fitness centroid metric allows us to capture the relationships between the multidimensional parameters that prescribe each condition. The metric has proved very useful for simplifying and visualizing the complex data that results from experiments, which seek to map a parameter space; similarly, this type of data compression may prove useful for quantitative comparison between strains and groups of strains. However, as centroids are a non-monotonic metric, this compression also results in a loss of information. Consider two strains: Strain A is more fit at low temperature but equivalent to the reference strain at high temperature; Strain B is equally fit to the reference at low temperatures, but exhibits a fitness deficit at high temperature. In the fitness centroid metric, both strains exhibit a preference for low temperatures, and would therefore overlap. In another pathological example, any strain with a symmetric fitness profile with respect to a two-dimensional parameter space would have the same centroid, regardless of whether fitness is at a minimum, maximum, or uniform at that point. This may of course be addressed by reporting additional metrics, such as mean fitness, or higher-order derivatives of the landscape. Nevertheless, particularly for the fitness landscape being examined in this experiment, the fitness centroid metric has proved to be a valuable analysis tool.
To verify that the fitness centroid metric correlates with the performance of strains across each condition, 100 high-performing members from each condition were highlighted on the centroid distribution map (
Finally, in addition to individual centroid calculations, we also calculated a mean fitness centroid from subsets of the knockout collection annotated on SGD for one of 1011 phenotypes assigned to at least 5 genes. Welch's t-statistic was used to determine whether a subset annotated for a specific phenotype was significantly shifted from the mean centroid of the whole population. In order to account for multiple hypotheses, all p-values were scaled by a factor of 1011. It should be noted that certain phenotype annotations have further sub-annotations (“Resistance to Chemicals” could be further sub-divided by chemical, “Competitive Fitness” could be further subdivided by media condition, etc.) but these sub-annotations were not considered in the present study. Significant phenotype annotations were identified along both the temperature magnitude and temperature frequency axes (
Competition Assay to Validate Fitness Centroid Hits:
To validate fitness results from the pooled library screen, we selected four library members from different regions of the fitness scatter plot: ΔHSP104, ΔKAP20, ΔAHA1, and ΔSWA2 (highlighted in red in
These individual deletion members were grown in YPD at 30° C. for 12 h, reaching early stationary phase. Each of the four strains were mixed 1:1 with the ΔHO control strain, then each co-culture was seeded into four eVOLVER vials (containing YPD+50 ug/mL carbenicillin+25 ug/mL chloramphenicol) at OD 0.05. Harnessing the programmable nature of eVOLVER, selection was applied identically as it was for the original pooled experiment. Cells were grown at 30° C. for 5.5 h, and then maintained in continuous turbidostat culture (OD 0.15-0.20). Programmed heat shocks of varying magnitude and frequency were initiated as soon as regular dilution events were underway in all cultures. The control code was slightly modified from the original experiment, such that each of the four co-cultures was exposed to four conditions from the original experiment (33° C./2 h, 33° C./48 h, 42° C./2 h, and 42° C./48 h). Two mL culture samples were taken every 24 h for two days and frozen at −80° C. Genomic DNA was extracted as described previously.
Relative fitness was determined using the frequency of both the strain of interest and the ΔHO strain as determined by qPCR (
Competitive fitness values for each strain of interest were computed using the frequency of the strain, specifically (1−freqΔHO), in place of a ratio between two strains. The fitness heatmaps created from the qPCR frequencies in the validation study largely agree with the fitness heatmaps created from the sequencing data in the original pooled experiment (
Catalog of Integrated Millifluidic Devices Developed:
Using the fabrication methods described in Example 15, we developed three different integrated millifluidic devices to carry out complex fluidic tasks for eVOLVER. These devices are showcased in three continuous culture applications with unique requirements for fluidic handling (see
8-Channel Vial Router Device (Used in all Fluidic Demos):
We developed a pneumatic valving schematic that routes fluid to and from eight different vials, termed the 8-channel vial router device (design shown in
As depicted in
8-Channel Media Selector Device (Used in Dynamic Media Mixing Demo):
The next integrated device, the 8-channel media selector, was developed in order to permit media mixing via sequential actuations of a syringe pump. As depicted in
In the ratio sugar sensing demonstration (
Vial-to-Vial Transfer Device (Used in Biofilm Prevention and Yeast Mating Demos):
The final integrated device developed in this study, the vial-to-vial transfer device permits media transfer of culture from any one eVOLVER vial to any other. In order to maintain sterility within the device, expanded cleaning options were needed as well. As depicted in
In the automated passaging biofilm prevention (
Abstract Commands to Automate Complex Fluidic Routines:
As evidenced by the complex descriptions of the fluidic routines above, there is a need for abstraction when sending commands to the integrated fluidic devices. Enumerating specific control elements seems feasible when dealing with a simple peristaltic array. However, this is extremely tedious when dealing with the valving schemes of integrated devices. Additionally, as numerous sequential events are often needed to carry out fluidic tasks in the integrated devices (e.g.: open valves, then pull syringe, then change valves, then dispense syringe), robust transition between sub-tasks is needed. A missed step could lead to mis-priming the syringe pump or incorrectly routing fluid into the wrong location. Both of these concerns are addressed with abstract fluidic routines encoded as scripts and functions on the Arduino microcontroller (
Dynamic Media Mixing for Ratio Sugar Sensing:
In order to demonstrate that fluidic multiplexing could be used to manage media composition for multiple cultures maintained by eVOLVER, we constructed an 8-channel media selector device that dynamically draws media from multiple input sources and addresses a defined mixture to a culture of choice. We used this to interrogate and characterize yeast galactose metabolic gene induction, which is known to respond to ratios of galactose and glucose (Escalante-Chong R., et al., Proc. Natl. Acad. Sci. U.S.A 112, 1636-41 (2015)) (
Glucose and galactose solutions were labelled with blue and yellow food coloring, respectively. These solutions were then used to supplement SC media (also supplemented with 50 mg/mL adenine hemisulfate) in a 4-fold dilution series of each sugar type (1%, 0.25%, and 0.06375%). We measured the 6 resulting medias and a sugar-free control on a Spectramax M5 plate reader spectrophotometer, using absorbance at 630 nm and 430 nm to estimate the component sugar concentrations independently (
For the first experiment, yeast cells harboring an integrated galactose-inducible reporter (pGAL1-mKate2, ySK499) were grown from frozen stocks in YPAD (YPD+50 mg/mL adenine hemisulfate) overnight, then diluted 1:100 into flasks containing SC+2% raffinose+50 mg/mL adenine hemisulfate and grown for 16 h in a shaking incubator at 30° C. We prepared seven different medias using color-labelled sugars as before: three SC+glucose medias (1%, 0.25%, and 0.06375%), three SC+galactose medias (1%, 0.25%, and 0.06375%), and a SC sugar-free control. By mixing any two of the seven medias, we could create 16 different SC+sugar compositions: three glucose-only (at 0.5%. 0.125%, 0.031875%), three galactose-only (at 0.5%. 0.125%, 0.031875%), nine different glucose/galactose ratios, and a sugar free control.
Yeast cultures were maintained in eVOLVER at the specified sugar compositions at a density window of OD 0.2-0.3 for 16 h. This was achieved using the 8-channel media selector device (
The second experiment was performed as above, but food coloring was excluded from the media so as to not affect cell growth in any way. Yeast cultures were prepared and seeded into eVOLVER vials as before, and maintained at the specified glucose/galactose ratio at a density window of OD 0.2-0.3 for 36 h. Culture samples were taken every 2 h for 16 h (with additional steady state timepoints taken at 24 and 36 h) to determine the induction rate of the galactose reporter. Flow cytometry was performed on fixed samples, and the percentage of mKate2+ cells was calculated by gating cells with mKate2 fluorescence higher than the t=0 control (representing the uninduced state in raffinose) (
Automated Passaging for Biofilm Prevention:
To demonstrate the utility of the millifluidic system for mediating liquid transfer between cultures, we designed a device capable of overcoming biofilm formation during long-term continuous growth experiments. To do so, we applied the vial-to-vial transfer device to continually passage cells into fresh culture vessels in an automated fashion (
An overnight culture of Serratia marcescens (ATCC 13880) was grown in pre-buffered LB Miller media (pH 7.2). This culture was used to seed two eVOLVER vials at OD 0.05. A control vial was maintained in a density window of OD 0.25-0.3 at 30° C. for 14 h before enough biofilm had deposited to affect density measurements. The second vial was grown for 9 h, at which point a 2 mL aliquot was transferred to a new vial containing fresh media using the automated vial-to-vial transfer device (Methods and
While simple in design, this result is impactful for enabling continuous culture for undomesticated microbes that have proved incompatible with routine continuous culture. It provides a non-chemical means to prevent biofilm, preventing possible toxic or other unintended effects on vegetative growth in the culture. This approach also flips the selection for biofilm in traditional continuous culture systems by, in fact, selecting against adherent cells over time. This mechanism has been implemented by a few single-purpose devices (de Crdcy-Lagard V. A., et al., BMC Biotechnol. 1, 10 (2001); de Crécy E., et al., Appl. Microbiol. Biotechnol. 77, 489-496 (2007)), but the flexibility of the eVOLVER platform permits other types of manipulations due to the customizable nature of the millifluidic devices. Finally, while the present experiment could be achieved through manual transfers, the benefits of an automated system are realized for longer-term experiments in which frequent manual transfers (every 8 h for this species) become burdensome.
Parallel Yeast Evolution and Mating in Automated Cell Culture:
Finally, we sought to apply millifluidic multiplexing in an experiment requiring coordination of multiple fluidic functions in an automated fashion. We sought to carry out parallel evolution in two haploid yeast populations, and programmatically mate cells at biologically relevant timepoints to harness sexual reproduction as a trajectory for adaptation (McDonald M. J., et al., Nature 531, 233-236 (2016)) (
Automated Yeast Mating Routine:
Overnight cultures of fluorescently labelled haploid MATα (ySK116) and fluorescently labelled haploid MATα (ySK743) cells were grown overnight in YPD (
Parallel Evolution of Antifungal Resistance and Mating:
Single colonies of fluorescently labelled haploid MATα (ySK116), fluorescently labelled haploid MATα (ySK743), and fluorescently labelled diploid control (ySK116x743) cells were grown overnight in YPD. These overnights were used to seed three eVOLVER vials at OD 0.05 (containing YPAD+50 ug/mL carbenicillin+25 ug/mL chloramphenicol). Cells were maintained at 30° C. in a density window of OD 0.25-0.3 for several generations in order to measure a baseline growth rate. At 16 h post-inoculation, cells from each haploid population were transferred by the vial-to-vial transfer device into the same fresh vial containing YPAD in order to create a new pre-drug to diploid population.
Next, cyclohexamide (CHX) was added to the haploid MATα vial, ketoconazole (KETO) was added to the MATα vial, and both drugs were added to the diploid control. Using the media selection portion of the vial-to-vial transfer device, 1 mL aliquots of media at 20× drug concentration were used to achieve a step-function transition to the final 1× drug concentration in each vial (0.2 ug/mL CHX for MATα, 6 ug/mL KETO for MATα, 0.2 ug/mL CHX+6 ug/mL KETO for the diploid control). Growth rate was continuously tracked, and was found to drop to roughly 10% of its original value over the 20 h following drug addition (
Two automatically triggered timepoints were taken: t1 (or “CHX recovery”) was triggered at 68.7 h by the MATα vial returning to 50% of its pre-drug growth rate; and t2 (or “KETO recovery”) was triggered at 98.1 h by the MATα vial returning to 50% of its pre-drug growth rate (
This experiment highlights another one of the advantages of an automated system over manual timepoints, as addressed in the automated passaging for biofilm prevention experiment. While manual sampling is easy for short routine experiments, automated sampling is extremely valuable over a long experiment when timepoints are frequent or unpredictable. Programming the sampling logic permitted us to sample cells at biologically-motivated timepoints (50% growth recovery) rather than arbitrary, schedule-motivated timepoints. Between remote real-time monitoring and programmable manipulations, the amount of time spent physically overseeing an experiment can be greatly reduced.
MIC Assay to Evaluate Antifungal Resistance:
To evaluate the degree to which evolved strains and the resulting diploids were resistant to each drug in isolation or combination, a variant Minimum Inhibitory Concentration (MIC) assay was performed on cells from each timepoint. Nine samples were across 64 combinations of cyclohexamide and ketoconazole: founder ySK116 and MATα samples from each timepoint: yBW004, yBW005; founder ySK743 and MATα samples: yBW006, yBW007; and the three diploid samples: yBW003, yBW008, and yBW009; see TABLE 2). 100 uL of each frozen stock created from post-selection cultures was thawed, added to 2 mL YPAD and then grown in culture tubes in a shaking incubator for 16 h at 30° C. Linear dilution series were prepared for each drug at 4× concentration in YPAD. In 96-well deep well blocks, 100 uL of each drug media was added to 200 uL of cells to bring every component to desired final concentration (0-0.56 ug/mL CHX, 0-14 ug/mL KETO, at OD 0.01). Each well received an estimated 60,000 cells from the non-clonal population that comprised each sample pool. The resulting 400 uL cultures were grown in a shaking incubator at 900 rpm and 30° C. for 24 h, then 200 uL of each culture was measured on a Spectramax M5 plate reader spectrophotometer. A blank measurement of cells at the seeding density of OD 0.01 was subtracted from the endpoint measurements to compute the change in optical density resulting from growth (
As expected, each haploid population developed a different antifungal resistance phenotype. Intriguingly, the CHX evolved pools exhibit a strong resistance phenotype that is specific to CHX, while the KETO evolved pools have a milder, more generalized resistance phenotype. Additionally, while CHX resistance is clearly passed on to the diploid pool, suggesting a dominant mutation, KETO resistance is not passed on, suggesting a recessive mutation in the haploids. There are numerous mechanisms by which resistance to either drug may be achieved (Anderson J. B., et al., Genetics 168, 1915-23 (2004); Kanafani Z. A. and Perfect J. R., Clin. Infect. Dis. 46, 120-128 (2008)). For the present study, we explored two possible avenues (see below).
It should be noted that as measurements were performed on pooled samples containing non-isogenic populations, the results may be influenced by the distribution of resistant cells in the population in addition to the resistance of any particular cells. While not a true MIC assay in the traditional sense, this assay still provides valuable information about the performance of the evolving populations over time.
We also note that cell density dependence is commonly observed in antibiotics and antifungals (Brook I., Rev. Infect. Dis. 11, 361-8)). This is likely responsible for the apparently contradictory result that the evolution experiment was performed at drug concentrations above the measured MIC of the founder strains. It also suggests additional utility for density tracking in eVOLVER, permitting cultures to be assayed for resistance to antibiotics, antifungals, or other stressors under conditions of tightly controlled density in a replicable manner.
Growth Rate Assay to Evaluate Antifungal Resistance:
As an alternative metric to evaluate performance of the diploids formed during the experiment, a growth rate assay was performed in media containing each drug in isolation or combination. Three diploid pool samples were assayed: yBW003 from to, yBWOO8 from t1, and yBW009 from t2. 100 uL of each frozen stock created from post-selection cultures was thawed, added to 2 mL YPAD and then grown in culture tubes in a shaking incubator for 16 h at 30° C. These cultures were used to seed duplicate eVOLVER vials containing media with one of four drug conditions (no drug, 0.2 ug/mL CHX only, 6 ug/mL KETO only, both 0.2 ug/mL CHX+6 ug/mL KETO) at an initial density of OD 0.1. Each vial received an estimated 3.2×107 cells from the non-clonal population that comprised each sample pool. Cultures were maintained in eVOLVER at 30° C. for 24 h without dilution, while tracking density continuously. Growth rate was calculated for each culture over the density range OD 0.2-0.8 (
Sequencing Antifungal Resistance Mutations:
We sequenced one potential mutational target for each drug in the evolved haploid lines. Primers were designed to sequence RPL41A/RPL41B, two paralog genes encoding the molecular target of CHX (Cokol M., et al., Mol. Syst. Biol. 7, 544 (2011)), and ERG3, encoding an enzyme that can confer resistance to azoles when mutated (Kanafani Z. A. and Perfect, J. R., Clin. Infect. Dis. 46, 120-128 (2008)). Genomic DNA was extracted from 100 uL aliquots of the following cultures: 1) MATα founder (ySK116), 2) MATα founder (ySK743), 3) three resistant clones from the CHX-evolved MATα population (yBW005), and 4) three resistant clones from the KETO-evolved MATα population (yBW007). The target genes were isolated from genomic DNA extracts by PCR using primers prCM353-360 (TABLE 3) in a 20 uL reaction with q5 polymerase (New England Biolabs) with the following cycling conditions: (i) denaturation: 95° C. for 10 min; (ii) amplification (30 cycles): 95° C. for 10 s, 62° C. for 10 s, 72° C. for 40 s; (iii) elongation: 72° C. for 7 min. Resulting DNA was purified using a DNA Clean Concentrator Kit (Zymo Research), and Sanger sequencing was performed. All three yBW005 colonies were found to have the same RPL41A/RPL41B sequences as the ySK116 founder, indicating that resistance is gained via a different mechanism. However, mutations in ERG3 were detected in all three yBW007 colonies, notably a nonsense mutation in ERG3 at amino acid 60 (
Chemostat Function Demonstration:
We sought to demonstrate that eVOLVER may be operated under different continuous culture regimes simply by changing the Python script. To do so, we devised and carried out a simple chemostat experiment without any modification to the hardware or Arduino scripts.
An overnight culture of Escherichia coli TG was grown in pre-buffered LB Miller media (pH 7.2). This culture was used to seed 12 eVOLVER vials at initial OD 0.05. The vials were maintained at moderate stir rate at 37° C. for 2 h before programmed dilutions were initiated. eVOLVER was programmed to continually dilute vials at six pre-set dilution intervals in duplicate. Pump intervals (2-8 min) and pump duration (1-4 s=1.15-4.6 mL) were set to correspond to dilution rates of 6 hr−1, 3 hr−1, 1.5 hr−1, 1.125 hr−1, 0.5 hr−1, and 0.375 hr−1. Density was tracked continuously for each replicate culture and overlaid (
While this experiment was designed to demonstrate that selection conditions can be changed with changes to the Python code alone, this is not the only way to enable chemostat functionality. In fact, communication burden can be reduced by abstracting repeated dilution routines by altering code on the auxiliary board Arduino controlling the pumps. This strategy has proved robust in preliminary work.
Most automated culture systems are designed to address specialized needs and are thus limited to active control over one or more predetermined culture parameters. We designed eVOLVER so that it could be flexibly configured to measure and control an arbitrary, user-defined set of parameters. In order to accomplish this, the system's hardware design emphasizes rapid, cost-effective scaling and customization, as well as accommodation of future technological advancements (
The Smart Sleeve is a manufacturable unit that mediates monitoring and control of growing cultures (
eVOLVER's fluidic module, which controls movement of media, culture, and liquid reagents within the system (
We developed a hardware and software infrastructure for eVOLVER that complements the scalability and configurability of the Smart Sleeve and fluidic modules (
An important feature of eVOLVER's design is its ability to leverage network connectivity to coordinate and run experiments over the internet. eVOLVER's distributed hardware architecture enables efficient transmission of large packets of high-dimensional, real-time data (
For example, during a typical data acquisition/control protocol, a scripted routine may query the Raspberry Pi every 30 seconds for Smart Sleeve-acquired culture status data (e.g. temperature, optical density, etc.) (
eVOLVER is capable of robust long-term operation. The system configuration described in this paper is capable of running long-term (250+h) experiments without electronics or software failure (
In order to showcase eVOLVER's ability to conduct long-term continuous culture laboratory evolution experiments at high throughput, we explored the relationship between culture density and fitness in evolving yeast populations. To accomplish this, we configured eVOLVER to function as a turbidostat, maintaining culture density within a constant, defined window bounded by lower and upper OD thresholds (ODlower threshold−ODupper threshold). Using continuously recorded OD data (
We then used eVOLVER to perform fitness measurements on isolates from each of the evolved populations by competing them against a fluorescently-labelled ancestor strain under low- and high-density continuous growth regimes (468 total cultures), assaying for population ratio over time using flow cytometry (Kryazhimskiy S., et al., Science 344, 1519-1522 (2014)) (see Example 6). The fitness distributions generated for low- and high-density regimes were distinct (
There is growing interest in understanding how an environment's temporal features shape organismal adaptation (Mitchell A., et al., Nature 460, 220-224 (2009)). A fluctuating environment may yield adaptations distinct from those selected under monotonic pressure (Ketola T., et al., Evolution (N. Y.) 67, 2936-2944 (2013); Sether B. E., and Engen S., Trends Ecol. Evol. 30, 273-281 (2015)). eVOLVER can be used to systematically vary temporal features of a selective pressure while holding other culture conditions constant. To demonstrate this, we performed growth selection experiments on a pooled YKO library (Giaever G., et al., Nature 418, 387-391 (2002); Li Z., et al., Nat. Biotechnol. 29, 361-7 (2011); Breslow D. K., et al., Nat. Methods 5, 711-718 (2008); Giaever G., and Nislow C., Genetics 197, 451-465 (2014)) (5,149 unique members) under conditions in which a single environmental variable—temperature—was temporally varied. A two-dimensional selection space was programmed by varying the magnitude and period of square wave temperature oscillations (
We computed a 2D weighted centroid for each library member by transforming its measured fitness into coordinates of temperature magnitude and frequency, allowing us to compare the library phenotypes across the 2D temporal selection space (see Example 6) (
Library members with significant fitness centroid shifts along the magnitude or frequency axes were identified (
In order to demonstrate the potential of the fluidic multiplexing framework to automate movement of reagents and cells in eVOLVER, we designed devices for three experimental applications: (1) dynamic media mixing during continuous culture to track yeast response to ratios of sugars (see Example 6); (2) preventing bacterial biofilm formation via automated passaging (see Example 6); and (3) programming sexual reproduction between adapting yeast populations (see Example 6).
We showed that fluidic multiplexing could be used to manage media composition for multiple cultures maintained by eVOLVER by constructing an 8-channel media selector device that dynamically draws media from multiple input sources and dispenses a defined mixture to a culture of choice (see Example 6) (
The utility of the millifluidic system for mediating liquid transfer between cultures was demonstrated using a device that overcomes biofilm formation during long-term continuous growth experiments (
Finally, we demonstrated that multiple functionalities can be combined to automate complex fluidic handling routines. We designed a fluidic routine for automated sexual reproduction of adapting yeast populations by integrating multiplexed media selection, vial-to-vial transfer, and post-transfer device cleaning (see Example 6) (
Here we present the eVOLVER platform, a multi-objective framework for diverse automated cell growth applications. The eVOLVER platform is designed for high-throughput real-time monitoring and continuous control over experimental parameters for large numbers of cultures in parallel. We define experimental parameters as the measurable and/or controllable aspects of the culture environment: for the experiments in this study, we focus on stir rate, temperature, optical density, and rate/composition of fluid flow, but these could be expanded to include any number of parameters (e.g. culture pH, other optical probes). For multiple experimental parameters, how is individual control over many culture vessels achieved? First, the key to individual control lies in equipping each culture vessel with a programmable “Smart Sleeve”, which contains the hardware required to measure and adjust each experimental parameter. Activity is customized and coordinated across Smart Sleeves in a robust, open-source hardware infrastructure capable of monitoring and regulating each parameter in real-time, outlined in Example 11. Second, developing eVOLVER as a network-based framework proves to be enabling in two ways (Example 12): (a) network connectivity/compatibility enables a single computer to efficiently communicate to and operate several devices in parallel, creating a distributed solution for scaling to large numbers of cultures: (b) implementing modern communication protocols to stream data unlocks the internet of things, empowering users with the ability to remotely monitor an experiment and compile data in cloud-based databases. In Example 13, we describe how to calibrate and control the core experimental parameters for the Smart Sleeve configuration employed in this study, as well as introduce the methodology for customizing (modifying, adding or subtracting) experimental parameters according to the needs of the user. In Example 14, we introduce the fluidic module of eVOLVER, which similarly to the Smart Sleeves, can control different configurations of liquid handling elements, such as an array of peristaltic pumps, to control fluid flow individually for each culture vessel simultaneously. Next, in Example 15, we describe a paradigm for programmable fluidic handling in continuous culture, achieved by novel millifluidic devices with integrated valves. Leveraging the eVOLVER fluidic module to control these devices, we vastly expand the capabilities of the system beyond simple input/output functions to complex fluidic manipulations for new continuous culture applications. In Example 16, we discuss specific strategies for implementing additional commonly sought functionalities in the eVOLVER framework. In Example 17, we address considerations for use of eVOLVER, including media requirements, network connectivity, device maintenance and lifetime, and prevention of contamination.
General Summary:
The eVOLVER hardware framework contains three levels of organization: (1) programmable sensors and actuators (e.g. Smart Sleeve components, pumps/fluidic control elements) (2) a Motherboard and microcontrollers, and (3) a Raspberry Pi. At the level of individual culture vessels, Smart Sleeves enable individual control over several experimental parameters in the culture (
Smart Sleeve:
The programmable Smart Sleeve is the foundational unit on which the eVOLVER is built (
The sensors and actuators on each sleeve are integrated in a small printed circuit board (PCB), termed the Component Mount Board (CMB). We designed the CMB such that we can easily solder electrical connections and efficiently manage/package wiring from the sensors. The CMB is a very simple PCB, containing only a few resistors, and is straightforward to redesign and inexpensive to manufacture, if needed. The simplicity in the CMB leads to robustness in the system. For example, any accidental overflow and spillage from the vials (e.g. from clogged fluid lines or user error) should minimally impact the rest of the system, as critical components are located at the Motherboard rather than the sleeve itself. Ribbon cables provide a modular way to connect the integrated Smart Sleeves to the Motherboard.
The CMB is designed to rest atop a 3D printed piece, which houses optical density and temperature components (see Example 13). The printed part can be fabricated with any commercial or DIY 3D printer, readily available at almost any university or hacker space, and customized to the requirements of the user. For example, if a user wanted to change the mode of optical density detection between scattering and absorption, they could redesign the 3D printed part housing the LED-diode pair such that it would have the correct offset angle for the desired mode of measurement.
Motherboard:
Forming the core of the hardware framework, the Motherboard is designed to be modular and enable individual control of an array of Smart Sleeves. In particular, PCBs can be designed to plug into the Motherboard for customization of how sensors are read or actuators are controlled. In the depicted setup, the Motherboard contains 7 customizable sensor/actuator slots (SA slots) that interface with the components of the CMB for each Smart Sleeve (
In more detail, two wires from each of the sensors/actuators on the CMB, bundled in a ribbon cable, are electrically routed through the Motherboard to one of 7 different SA slots. A total of 224 wires (7 SA slots×16 vials×2 wires) is required to properly route all sensors/actuators to the correct SA slots. Each SA slot consists of an array of 70 metal female pins. A PCB with the correct male pin layout would be able to plug into a slot, namely the customizable control boards (
Arduino Microcontrollers:
The 7 customizable SA slots are organized under 4 SAMD21 Arduino Mini microcontrollers. This layout permits control over 4 different experimental parameters. Experimental parameters are controllable characteristics of the culture, such as the temperature or stir rate. The control of one parameter often requires more than one sensor and/or actuator, and thus requires more than one SA slot. For example, to control the temperature (parameter) of the culture, one SA slot is used to measure temperature (sensor, interfaces with ADC board) and another is used to heat the culture (actuator, interfaces with PWM board). The boards at these two SA slots are controlled by a single microcontroller to efficiently coordinate SA activity (e.g. sequential tasks, fast feedback control).
Managing several experimental parameters simultaneously across several cultures is a non-trivial, data-intensive task. The use of multiple microcontrollers permitted us to divide the load by experimental parameter for functional parallelization. Our design is analogous to that of personal computers, where specialized functionalities are enabled by modular supplementary electronics (e.g. graphics cards) which interface on a single motherboard. This approach was crucial for managing the complexity inherent in defining multidimensional growth environments.
Importantly, this design also facilitates modifications to the system by segregating each function, analogous to reconfiguring or swapping out components in a desktop computer. When modifications are made (either to microcontroller software or to the boards interfacing at the SA slot), adjustments are confined within a single experimental parameter and do not impact any other parameters of the system.
16-Channel Pulse Width Modulation (PWM) Board:
One of the customizable control boards, the PWM board is designed to plug into the Motherboard and enable an Arduino to easily and quickly control many actuators (e.g. motors, LED, heaters) in parallel. For example, 16 individual LEDs can be connected to the PWM board and each of the LEDs can be set to a different brightness and updated to a different value in fractions of a second. The board has two main functions: (1) amplifying the 3.3V signal from the Arduino to a higher output voltage (5V to 24V), depending on the voltage source, and (2) expanding the Arduino pulse width modulation (PWM) capabilities from 3 to 16 channels. PWM is essential since it allows digital signals to have a more analog-like output. Analog-like outputs enable finer control of experimental parameters. For example, the temperature control of the system would be noisier if the input was toggling between the heaters fully on and off. With PWM, the user can instead use a simple PID controller to feedback from temperature measurements to optimize for a specific, highly controllable heat output. The PWM board can be daisy chained such that a single Arduino can in principle control hundreds of channels.
16-Channel Analog Digital Converter (ADC) Board:
Another customizable control board, the ADC board is designed to plug into the Motherboard and measure the signal from dozens of sensors in the system. The sensors currently integrated in each sleeve are simple and can be measured with basic voltage divider circuits. The sensor and resistor are placed in series, and the voltage across a resistor changes when the measurement from the sensor changes. The board reads this voltage and has two main roles: (1) remove noise from the signal through a low pass filter and (2) multiplex the signal from all 16 channels to one analog input pin on the Arduino. When the signal arrives at the input pin, the Arduino changes the analog signal to a digital signal via its own 12-bit ADC.
Communication and Parallelization Via Raspberry Pi:
Most laboratory equipment is controlled via local serial communication. Likewise, in eVOLVER, a RS485 serial communication protocol is used internally to send updated parameter values and receive current measurements to/from the Arduinos. All serial communication occurs on the same channel. In typical lab equipment, the data is then transmitted to a local computer via USB, usually requiring the equipment and computer to be in the same physical location. This may work well for a single system, but physical limitations arise when trying to scale to high-throughput studies on multiple systems (e.g. 100's of vials). Consequently, a solution was needed to scale the interface between many eVOLVER units and a computer/server monitoring the experiment.
To address this, eVOLVER is designed as a network-based tool, operating similar to how servers and computers communicate within the same network at a university or company. Each 16-vial eVOLVER unit contains one Raspberry Pi, a small Linux board, that helps relay information from the device back to the computer via an Ethernet port. Performing three main functions, the Raspberry Pi board: (1) enables the system to easily interface with modern internet protocols, (2) monitors and updates the Arduino microcontrollers with the desired configuration settings (e.g. temperature set points, fluid commands), and (3) gathers data from the Arduinos for user consumption. This enables a single laboratory computer/server to run many eVOLVER units distributed across physically different locations (e.g. different rooms, floors), since the devices can be connected via router (
Directing Customizable, Repeatable, and Shareable Experiments with Software:
Each time a user runs a unique eVOLVER experiment, a new custom Python control script is generated. This has two major implications for running continuous culture experiments. First, experiments are easily customizable by simply adapting the Python code to meet the needs of the experiment without changing any of the hardware components. This approach is used throughout this study, for everything from defining different experimental conditions (e.g. optical density windows, dynamic temperature profiles), to feedback between parameters (e.g. turbidostat dilution calculations), to incorporating higher-level computations (e.g. growth-rate dependent event triggering). Second, experiments are easily replicated by simply copying the Python script from one experiment to a new file. By using the exact same control algorithms, the conditions can be tightly matched between experiments. This approach is used throughout this study, particularly for validation experiments, in which a strain isolated from an evolution or genetic screening experiment is then tested in the same environmental conditions from which it was isolated. Furthermore, this permits sharing protocols between users who have the same hardware layouts. Not only is this advantageous for collaborations between labs and replication studies, but it also accelerates the rate at which new users can learn the device. Basic experiment control scripts are made available at fynchbio.com; online code repositories may be used to collect and share additional, user-generated experiment control scripts.
Connecting Biological Laboratory Equipment to the Internet of Things:
Typically, in the laboratory, experimental data is collected, analyzed, and stored in local files on a user's computer. Each user has their own preference or standard procedure to analyze and display the data, making sharing and curating information difficult. Consequently, though potentially valuable, raw data is infrequently shared. The eVOLVER framework offers a solution to this problem. Since eVOLVER uses modem communications protocols, the device can stream data directly to a database and utilize cloud tools. This can facilitate how experiments are done in several ways: (1) real time monitoring of experiments from anywhere with an internet connection. (2) standardization and curation of growth data between experiments, and (3) interfacing with cutting-edge cloud tools for analysis and segmentation of data. These aspects of the eVOLVER framework promote scalability even beyond high-throughput experiments, facilitating modern ways of ingesting and analyzing data. Towards this end, we have developed back-end code and a Python library to easily stream data from eVOLVER to a server for real time visualization and analysis of data. More information can be found at fynchbio.com.
Customizability was a key design consideration when developing the eVOLVER. In Example 12, we describe the utility and ease of writing software to program feedback between experimental parameters for designing novel experiments. In this section, we describe how one can customize the hardware to modify/add parameters of interest. Additionally, we present the components and systems used for the measurement and control of three core experimental parameters in each Smart Sleeve: stirring, temperature, and optical density.
Modifying, Adding, and Removing Experimental Parameters:
A key feature of our hardware framework is that it enables adding, subtracting, and modifying components without changing the rest of the system (
Specifics for implementing additional commonly desired parameters and functionalities in eVOLVER are found in Example 16.
Stirring:
The eVOLVER platform features tunable and independent stir rate control across culture vials. Stirring in eVOLVER is actuated by 12V brushless DC motors with attached neodymium magnets. The fastened magnets spin a stir bar (20 mm×3 mm, PTFE coated) within an autoclaved glass vial (28 mm×95 mm, borosilicate). The stirring module utilizes a single SA slot on the Motherboard; in the particular configuration described in this study, we utilized SA slot 1 (
Temperature:
In contrast to current approaches in which all culture vessels are housed in a single incubator (Toprak E., et al., Nat. Protoc. 8, 555-567 (2013); Takahashi C. N., et al., ACS Synth. Biol. 4, 32-38 (2015)), we developed a module for individually controlling the temperature of each Smart Sleeve in the eVOLVER. This not only allows the cultures to be maintained at distinct temperatures, but also reduces thermal mass, permitting dynamic temperature profiles. For the configuration described in this study, the temperature module utilizes SA slots 2 and 3 on the Motherboard (
Typically, there are three main components to temperature control: (1) a thermometer, (2) a heater, and (3) a feedback controller. In our setup, the thermometer and the heater are integrated in the Smart Sleeve while the feedback controller is located on the Motherboard. Specifically, the temperature is measured by a 500 μm thick temperature-sensitive resistor, or thermistor (Semitec, 103JT-025). The sensor is integrated into the sleeve between the 3D printed part and the aluminum tube, and the thermistor is soldered onto the component mount board (CMB) after assembly. The aluminum tube enables even heat distribution/dissipation and shields the culture from ambient light (important for other measurements/parameters). Two heating resistors (20 Ohm 15 W, thick film) are screwed onto the aluminum tube for better contact and connected to the CMB via soldering. In our setup, the four leads, 2 from heating resistors and 2 from thermistor, are connected via a ribbon cable to the Motherboard and routed to SA slots 2 and 3, respectively. In slot 2, a 16-channel PWM board amplifies a 3.3V signal from the Arduino microcontroller to a 12V signal to actuate the heating resistors. Slot 3 contains a 16-channel ADC board, which reads the voltage difference across a 10 kilo Ohm resistor, and is responsible for analog filtering and demultiplexing the signal from the thermistor. These slots are connected to and are programmatically controlled by Arduino. Briefly, the Arduino code interprets serial inputs from the Raspberry Pi, updates the set point on the PID controller, and responds with the current measured temperature. Temperature settings can be updated as frequently as every 30 seconds. To determine how much to turn the resistive heaters on, the Arduino is programmed with a simple PID control algorithm. The PID controller can be easily tuned via software to obtain the desired overshoot and time delays. The Arduino then controls a PWM board (on SA slot 3) to interface with the resistive heaters and get the desired heat output.
Calibration of the temperature measurement in the sleeve was performed by comparing the temperature of water measured in the vial using a thermocouple to the values returned by the thermistor (
It should also be noted that at different temperatures, the optical density readings are affected accordingly. This effect was measured in both yeast cultures and evaporated milk (
Optical Density:
Based on previous work (Toprak E., et al., Nat. Protoc. 8, 555-567 (2013)), optical density measurements in a bioreactor can be measured with a simple 900 nm infrared (IR) LED and photodiode pair. There are two practical benefits of using 900 nm scattered light instead of the classic OD600. First, at 900 nm, turbidity/optical density measurements are less dependent on the absorbance spectrum of the media, meaning calibration is required less frequently before each experiment. Second, wavelengths in the visible range are preserved for light induction and colorimetric assays. To maximize scattering, the LED-diode pair is offset at a 135° angle. The 3D printed part is designed to house the LED-diode pair slightly above the height of the stir bar, at the correct angular offset. The part can be easily customized and printed to the users required specifications with any 3D printer.
In the eVOLVER configuration used in this study, the IR LED and photodiode pair (4 leads) are each connected to the CMB via screw terminals in SA slots 4 and 5, respectively (
For convenience, density readings from the 900 nm LED-diode pair were calibrated to OD600 measurements from a spectrophotometer, and the calibration curve fit with a sigmoidal function (
As previously mentioned, varying temperature induces a shift in the optical density readings (
In this section, we describe the implementation of an additional board, separate from the main Motherboard, that we developed for fluidic control of each culture vessel. This design enables individual control over the liquid handling, just as Smart Sleeves enable individual control over other culture parameters. However, consolidating these components into a dedicated board facilitates changing between different modes of liquid handling.
Automated cell culture relies on programmable input/output of culture media. Fast and accurate, peristaltic pumps are typically used for this application (Toprak E., et al., Nat. Protoc. 8, 555-567 (2013)). For a single media input, a culture vessel requires two peristaltic pumps, one for influx and one for efflux. The influx line routes the media from the source into the culture, and the efflux line takes out waste media to maintain a fixed volume. Timing and coordination of these pumps is important for any automated cell culture application. A single input/single output system is the most basic type of fluidic control, and yet applying this scheme to a large number of independently-controlled culture vessels can prove challenging. To address this, and in anticipation of wanting to access even more complex fluidic functions, we developed a dedicated Auxiliary Board, separate from the Motherboard. The auxiliary board can simultaneously and independently control up to 48 fluidic control elements and supports much-needed abstraction of fluidic routines. The auxiliary board facilitates simple input/output functions at scale and accommodates more sophisticated fluidic solutions (see Example 15).
The auxiliary board is designed to receive serial inputs from the Raspberry Pi, translate abstract commands into simple sequential tasks, and simultaneously control up to 48 fluidic elements (
In this study, we applied this common hardware architecture (
In the basic eVOLVER setup featured in
A small degree of scalability is possible with the basic fluidic scheme of using the auxiliary board to control individual peristaltic pumps for each fluidic line. For example, running 16 vials in a typical two-input experiment (such as morbidostat-like experiments (Toprak E., et al., Nat. Protoc. 8, 555-567 (2013): Toprak E., et al., Nat. Genet. 44, 101-105 (2011)) would utilize all 48 channels of the auxiliary board, with three pumps per vial: two for influx and one for efflux. To permit further scaling of fluidic tasks without significantly increasing the number of pumps necessary, we developed a different paradigm of millifluidic handling for automated cell culture (complex fluidic scheme), which utilizes fabricated pneumatically-valved integrated devices (see Example 15).
A key development in microfluidics was the design and fabrication of devices containing integrated (pneumatic) valves that could allow for complex fluidic manipulations with minimal number of control elements (Unger M. A., Science 288, 113-116 (2000); Thorsen T., et al., Science 298, 580-4 (2002); Melin J. and Quake S. R., Annu. Rev. Biophys. Biomol. Struct. 36, 213-231 (2007)). Here, we describe (1) why adapting this technology for the macro scale is valuable for automated cell culture, (2) challenges faced when scaling to larger flow rates, and (3) a new framework for fabrication and bonding of millifluidic devices featuring integrated pneumatic valves. These devices offer a scalable solution to challenges faced by traditional fluidics.
Importance of Complex Fluidic Tasks:
The ability to program complex fluidic tasks could enable entirely new manipulations in automated cell culture applications (see
In electronics, custom circuits can be readily created by breadboarding; however, this approach scales poorly to larger, more complex circuits because it relies on tedious manual assembly and leads to limited durability. Similarly, fluidic systems consisting of flexible tubing connecting separate control elements, like pumps and valves, can solve simple fluidic tasks. However, the number of necessary fluidic connections scales with the complexity of the desired task. For example, even with an optimal valving scheme, the ability to perform automated large-volume transfers between any two culture vials in a 16-vial eVOLVER unit would require almost 300 fluidic connections and over 20 control elements. As in breadboarding, each connection would need to be routed individually by fluidic tubing and often by hand, a tedious task. Additionally, the tubing is usually fairly long, and each connection introduces dead volume, making the system less robust and impractical. Instead, by creating integrated (pneumatically-valved) schematics, we sought to make a millifluidic equivalent of a printed circuit board; the complex fluidic connections are now integrated in a small device that is computer designed, manufacturable, and much easier to reproduce. With the flexibility of CAD, one would be able to customize a fluidic device to fit their particular experimental needs.
Fluidic Scaling Problem:
The cost of control elements and assembly time of bioreactor units can prove to be a significant burden as one scales fluidic inputs and outputs for high-throughput operations. As previously described, most designs rely on a pinch valve or a peristaltic pump to separately control each of the media sources and another to control waste. For example, a single vial turbidostat unit with 4 different media inputs would require 5 pumps. A hypothetical 48-vial unit with the same capabilities would therefore require 240 pumps, at a cost of $7,000 to $10,000. The key problem is that the number of control elements increases linearly with the number of vials. Our pneumatically-valved devices can leverage concepts developed in microfluidics in order to scale throughput by multiplexing and demultiplexing inputs and outputs (Thorsen T., et al., Science 298, 580-4 (2002): Grover W. H., et al., Sensors Actuators, B Chem. 89, 315-323 (2003)). In this scheme, the number of controllable vials scales exponentially to the number of control elements, needing only 30 elements to route up to 16 different fluidic inputs to 48 vials. We project the costs of this new hypothetical 48 vial fluidic schematic to be $1,000 to $2,000, roughly a 90% decrease in cost in comparison to current systems.
Desired Characteristics and Properties of Integrated Millifluidic Devices:
Though inspired by microfluidic technologies, integrated millifluidic devices for continuous culture have drastically different design requirements. The following is a list of critical requirements for this technology in the context of continuous culture:
Fabricating Integrated Millifluidic Devices for Automated Cell Culture:
Fabrication techniques used for microfluidics (e.g. photolithography, surface treatments) do not simply translate to larger dimensions. To scale devices to the millifluidic scale, an entirely new fabrication method is required. First, reagents for photolithography are optimized for channel heights of 1 to 300 microns. To reach the desired channel height of ˜1 mm would involve tediously stacking photoresist layers together, which requires precise alignment of photomasks. Second, the chemical glues, like silanes, that are typically used to functionalize plastic and silicone rubber sheets for bonding are difficult to apply uniformly across a large area (e.g. 10 cm×20 cm). Any small pocket where bonding was incomplete compromises the integrity of the entire device. Finally, the ability to prevent bonding in specific areas of the device (i.e. the pneumatic valves) is also critical, yet difficult with current techniques. Since there can be hundreds of integrated valves that must be protected from bonding, the ability to denote where the bonding occurs via a CAD drawing, instead of by hand, is critical to robust fabrication of the device.
To fulfill the design criteria previously listed, we developed a simple, robust prototyping method for fabrication and selective bonding of devices for fluidic control on the eVOLVER platform (
For proper pneumatic valving in the devices, an airtight seal must be formed between all layers. To bond the layers together, an optically clear laminating adhesive sheet was used (3M, 8146-3). The adhesive comes as a sheet sandwiched between polyester backings to maintain integrity of the adhesive. First, the PETG layers are plasma treated for 1 min with atmospheric gasses at MAX setting (Harrick Plasma, 30 W Expanded Plasma Cleaner) to promote adhesion between the adhesive and plastic. Adhesive (with one side of the backing removed) is quickly placed onto the activated surface and any bubbles are quickly rolled out. The PETG sheets with adhesive are then patterned with a laser cutter. To get a deeper cut without melting the plastic, the same design was cut three times (20% Speed, 100% Power). For selective bonding of the device, low laser power is used to raster off the adhesive but not cut into the plastic (70% Speed, 50% Power). Bonding of the PETG layers to the silicone rubber is accomplished by plasma treating the silicone rubber sheet and subsequently applying the sheet onto the adhesive. Clamping the two layers between two ½″ metal plates immediately after plasma treatment helps in bonding the two surfaces.
To interface with the device, barbed-to-thread polypropylene connectors (Value Plastics, X220-6005) were fastened into 10-32 threaded holes on the thicker control layer. 3 mm vias were punched into the silicone membrane to connect the flow layer to the barbed connectors on the control layer. The entire fabrication process, from a CAD drawing to a completed device, can be done in 3 hours.
Characteristics of Pneumatic Valves for Millifluidic Devices:
Our pneumatically-valved millifluidic devices enable customizable, programmable routing of liquid at volumetric flow rates of ˜1 mL/sec. The valves pinch off fluid flow on the flow layer when 10 psi is applied to the control layer and enable flow when vacuum is applied (
Coordinating New Fluidic Experimental Parameters:
Fluidic tasks in eVOLVER are enabled by the sequential actuation of valves in a specific fluidic network encoded in the integrated millifluidic device. We demonstrate these fluidic manipulations in a series of experiments (see
Software routines to control the control elements (valves and pumps) are also divided into commonly repeated functions, usually in a similar manner to how fluidic modules were divided. The code for each fluidic function is preloaded into the Arduino to coordinate tasks between each fluidic module. For example, a simple dilution event would first actuate valves in the media multiplexer to select media, then actuate a syringe pump for metering the desired volume, followed by valves in the vial demultiplexer and multiplexer to route media into the vial and remove efflux. By loading the routine for abstract functions (e.g. dilute, clean, vial-to-vial transfer) into the Arduino, robust communication can be ensured, with rapid transition between sub-tasks and no skipped steps (which could cause incorrect media routing, mis-priming of the syringe pump, or leaks and other device failures).
Rather than propose a single continuous culture device designed to a specific purpose, our goal with eVOLVER was to demonstrate a design framework that gives the user the freedom to imagine and carry out virtually any type of experiment that uses automated cell growth functionality to study cellular fitness. Here, we comment on reconfiguring eVOLVER for several experiments of significant interest in the community. For further examples, see
Chemostat:
As one of the simplest forms of continuous culture, small-volume (˜mL) chemostat arrays have been popular in directed and experimental evolution (Hope E. A., et al., Genetics 206, 1153-1167 (2017); Esvelt K. M., Nature 472, 499-503 (2011)). To run eVOLVER as a chemostat, one would use the peristaltic pump array with dilution events triggered by a programmed timer, rather than by optical density (as in turbidostat mode). This alteration can simply be made, without hardware changes, on the Python code (see
Morbidostat:
Morbidostat algorithms have been developed that gradually increase the selection pressure of an evolving culture, typically based on measured growth rate (Toprak E., et al., Nat. Genet. 44, 101-105 (2011)). Previously, this algorithm has been implemented with two media inputs (+ and − drug), requiring three peristaltic pumps per culture (w/efflux pump). In a 16-vial eVOLVER unit, this setup can easily be implemented by (1) controlling 48 pumps with the auxiliary board or (2) using multiplexed fluidics with the millifluidic devices. The prior being simpler to implement for 2 media inputs and the latter letting one scale to >2 inputs. As currently designed, the auxiliary board can control up to 48 fluidic elements (pumps/solenoids). To run morbidostat mode, one would need to modify the Python code to the desired growth algorithm (e.g. control rate of drug increase, growth rate threshold to trigger the drug input).
Optogenetic Control During Continuous Culture:
Light inducible protein domains have been used to dynamically control and rapidly prototype genetic networks (Milias-Argeitis A., Nat. Commun. 7, 12546 (2016): Olson E. J., et al., Nat. Methods 11, 449-455 (2014)). Hardware for light inducible systems typically rely on batch culture, limiting experiments to a narrow time window in which all cells across an experiment are in exponential phase. Attempts at coupling continuous culture to light induction have been limited by throughput (1-2 cultures) and reconfigurability. Equipped with components for light induction, eVOLVER would uniquely enable long-term optogenetic perturbations in finely controlled growth phases across a large number of culture vessels. Due to the modularity of eVOLVER hardware components, integrating optogenetic control is straight forward and requires minor modifications to the system. Details on integrating LEDs into eVOLVER is described in more detail as the example modification in Example 13.
Fluorescence Measurements:
Bulk fluorescence measurements have previously been demonstrated by Takahashi et al. during continuous culture, without the use of a photomultiplier tube (PMT) (Takahashi C. N., ACS Synth. Biol. 4, 32-38 (2015)). To recapitulate this setup in eVOLVER, an extra LED-diode pair would be added to the 6th and 7th S/A Slots, similar to adding LEDs for light induction. Additionally, the 3D printed part would be modified to house optical filters for better detection of any fluorescence signal. Potential setbacks in this setup (without a PMT) include potentially a low signal to noise ratio. This can be solved by multiplexing signal from all cultures into a single PMT via fiber optics. The electronics controlling the PMT would communicate back to the same RS485 line to be controlled by the same Raspberry Pi, similar to the auxiliary board. Alternatively, single cell fluorescence measurements would also be made possible by interfacing eVOLVER with a pipetting robot, droplet microfluidics, or using the native pump from the flow cytometer sample directly from the cultures. These systems could interface serially with the Raspberry Pi via RS485/USB or the lab computer via USB.
Different Culture Volumes:
As described in this manuscript. 40 mL culture vessels were chosen for a sufficiently large population size for full coverage of the genome during evolution. Other applications like bioproduction, larger library screens, or applications with expensive culture medium might be better suited with alternative volumes. The modularity of the eVOLVER framework enables redesign of the Smart Sleeve with limited changes to the rest of the hardware. For example, to design a sleeve for larger volumes, one would (1) machine a new aluminum casing, (2) obtain a fan/motor capable of stirring a larger/smaller volume, and (3) redesign a PCB/3D printed piece to optimize position of components (orientation of diode/LED for O.D. measurements through larger volume). The rest of the hardware downstream of the Smart Sleeve could potentially remain the same or have only slight software modifications, depending on size of culture vessel (e.g. tune PID controller for larger thermal mass).
With the appropriate parts (e.g. boards, electronic components etc.) and appropriate DIY manufacturing equipment (e.g. 3D printer, laser cutter), a 16-vial platform could be built in a week for $5,000. However, beyond assembly of an eVOLVER device itself, there are several other challenges to be considered prior to routine use of eVOLVER.
Media Requirements:
A primary consideration when running eVOLVER experiments is predicting the media consumption rate, which is typically orders of magnitude higher than in typical batch experiments. The required media for continuous culture can be estimated using the following equation:
It should be noted that additional media (˜20 mL) is needed during device setup to flush media input lines.
Network Connectivity:
The simplest eVOLVER setup communicates to the lab computer (running Python) within a local network via an Ethernet connection. Examples of the local network are a user's personal router or the building's router at the institution. It is recommended to contact local IT services for site-specific configurations. This setup does not require connectivity to the internet but is convenient to have. Empirically, we have observed disruption in communication with eVOLVER during and immediately after use of remote desktop software to access the lab computer, use of this software is not recommended with eVOLVER. A more scalable, reliable solution (running ˜100s of vials) would be to use a dedicated server to host the Python scripts and visualizations software (instead of a lab computer). These cloud services can be hosted by the university or externally via commercial providers (e.g. Amazon Web Services) and accessed on a browser on any computer. Again, we recommend contacting IT specialists for secure methods of communication between eVOLVER and the server at your institution. An Ethernet connection (instead of Wi-Fi) is recommended for all configurations.
Maintenance:
We have monitored the lifetime of eVOLVER components since the invention of the device (˜3 years). As in most systems, mechanical parts have the highest possibility of wear and thus need replacement most frequently. Likewise, in eVOLVER, the peristaltic pumps used have a lifetime of ˜6 months of typical use during continuous culture. Specifically, the silicone tubing within the head of the pump is frequently compressed when actuating peristaltic pumps and will tear over time. The head can easily and inexpensively be replaced (˜$4/pump). The computer fan used for stirring has robustly operated continuously for >3 years, whereas the magnetic stir bars are rated for a limited number of autoclave cycles and need eventual replacement. All other components (e.g. heaters, thermistors. LEDs, diodes, PCB, power sources) have been stably operating for >3 years.
Contamination Prevention:
While batch culture techniques often utilize biosafety hoods or flame convection currents to keep workspaces sterile, these are rarely amenable for automated cell culture devices. Prevention of contamination in eVOLVER is achieved at three levels: 1) sterilization of media, culture vessels, and fluidic lines, 2) attention to sterile technique, and 3) physical and chemical barriers to contaminants. First, all media bottles and their adapters, and all components of the culture vessel (e.g. borosilicate glass vial, magnetic stir bar, cap with fluidic adapters) are designed to be autoclaved before each use. Fluidic lines on the device are sterilized before and after each experiment using bleach and ethanol (see Example 6). Second, following sterilization, sterile technique should be practiced when attaching media lines to culture vials by working quickly, avoiding physical contact with the ends of fluidics lines, and taking care to spray gloves with ethanol. Finally, additional physical and chemical measures may be taken depending on the organism and experiment. For example, the sampling port may be covered by a sterile membrane for long-term culture. For slow-growing eukaryotic cultures, antibiotics can be added to the media to exclude bacterial contaminants. UV sterilization of components or surfaces is another preventative measure to consider.
Safety while Operating eVOLVER:
eVOLVER electronics modules are connected to high amperage power supplies. Electronic components may be exposed if improperly constructed and extreme care should be taken to protect against shock.
Many biological experiments require varying amounts of customization to fit the experimental needs of the researcher. However, currently, most laboratory equipment is single-use, proprietary and expensive, making customization difficult. Consequently, even simple experiments are usually designed around the capabilities of the equipment, at times severely limiting how experiments are run. In the last decade, do-it-yourself (DIY), open-source electronics have risen significantly in popularity due to falling costs of printed circuit board (PCB) manufacturing and the maturing open-source community. This cultural shift has resulted in creative, useful, and customizable tools such as 3D printers and small Linux computers (Raspberry Pi). These open-sourced technologies have changed how manufacturing, networking, and prototyping are approached, making it easier to build tools to exactly fit one's needs.
Similarly, the DIY culture has the potential to significantly impact biological experimentation. In fact, university labs are well suited to driving innovation with open sourced lab tools due to: (1) local technical competence, (2) resources (e.g. infrastructure, financial), (3) highly specific/diverse experimental needs, and (4) desire to share and replicate one's work. Despite these apparent synergies, DIY tools have not been widely used in biological laboratories, and ad hoc tools built in academic labs are often not robust enough for widespread adoption; often this decision comes down to valuing robustness over customizability. In order for DIY tools to be reliable, there must be a framework or standard that is robust, yet still allows flexibility and creativity for the user. For example, Arduino boards are a hardware framework/footprint to house a common micro-controller (ATmega328P). This framework became a critical standard for the community and allows hobbyists to easily program and interface with the micro-controller for their own application (e.g. drones, 3D printers). With this and other DIY successes in mind, we sought to develop eVOLVER as a modular, open-sourced hardware framework that is robust enough for widespread adoption, while still customizable for particular applications.
eVOLVER achieves the goal of creating a standardizing framework for automated cell growth experiments. The system is designed from the bottom-up to be customizable and expandable; its DIY infrastructure provides researchers with the ability to design, easily build out, and share new experimental configurations and data. eVOLVER's design utility is manifested at the component level: in Smart Sleeve design configurability, in the ability to specify custom liquid manipulation routines using the fluidic system, and in the modularity and composability of the hardware and software systems (
The results reported in this study establish eVOLVER as a scalable framework for realizing large-scale, multidimensional selection experiments to study, characterize, and evolve biological systems. The system's configurability enables precise specification of culture environment on an individual culture basis. By systematically co-varying parameters, eVOLVER can be used to investigate cellular fitness along multidimensional environmental gradients, potentially allowing for experimental decoupling of overlapping selection pressures. The ability to arbitrarily program feedback control between culture conditions and fluidic functions allows the user to algorithmically define highly specialized environmental niches.
We demonstrated the breadth of eVOLVER's experimental versatility in a series of showcase experiments. First, we used eVOLVER to conduct an experimental evolution study (
In a second experiment, we performed growth fitness experiments on a YKO library under systematically varied temperature fluctuations, demonstrating that eVOLVER could be used to extract unique fitness information across a temporally varied selection surface (
Accurate fluidic manipulation is a critical feature of continuous culture automation, but past approaches to fluidic routing have imposed experimental limitations. Inspired by electronic and microfluidic technologies (Thorsen T., et al., Science 298, 580-4 (2002)), we addressed this issue by developing a novel millifluidic control paradigm for programmatic routing of fluids during continuous culture. Our devices expand the repertoire of cellular manipulations available to eVOLVER while packaging functionality into a compact physical footprint. To highlight the utility and robustness of the devices, we performed three experimental demonstrations: sophisticated fluidic mixing and dispensation, vial-to-vial transfers, and integration of multiple devices for more complex culture routines (
We foresee the eVOLVER platform as an enabling tool for several emerging fields of research. eVOLVER could play an important role in investigating the adaptive basis of social behavior in microbial consortia. For example, the system's throughput, control, and fluidic capabilities could be leveraged to systematically test contributions of individual species to community fitness, potentially offering insight into how to construct ecologically stable communities from the bottom up (Friedman J., et al., Nat. Ecol. Evol. 1, 109 (2017)). In synthetic biology, designing regulatory circuits that minimize fitness cost to the host cell remains a major challenge3. Leveraging its ability to carefully monitor population fitness in high throughput, eVOLVER could be used to identify circuit design features that maximize evolutionary stability. This would be useful for applications requiring engineered cells to retain circuit function over many generations, and would be especially valuable for high-throughput testing of bioproduction strains prior to scaling-up to industrial bioreactors (Alper H., et al., Proc. Natl. Acad. Sci. U.S.A 102, 12678-83 (2005)). Relatedly, eVOLVER could be used to aid in the design, testing and optimization of synthetic microbial genomes (Hutchison C. A., et al., Science 351, aad6253-aad6253 (2016); Richardson S. M., et al., Science 355, 1040-1044 (2017)). Finally, we envision eVOLVER as enabling platform for building a community of users that can freely conceive, build, execute, and share experiments.
All of the features disclosed in this specification may be combined in any combination. Each feature disclosed in this specification may be replaced by an alternative feature serving the same, equivalent, or similar purpose. Thus, unless expressly stated otherwise, each feature disclosed is only an example of a generic series of equivalent or similar features.
From the above description, one skilled in the art can easily ascertain the essential characteristics of the present disclosure, and without departing from the spirit and scope thereof, can make various changes and modifications of the disclosure to adapt it to various usages and conditions. Thus, other embodiments are also within the claims.
While several inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the inventive teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.
All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.
All references, patents and patent applications disclosed herein are incorporated by reference with respect to the subject matter for which each is cited, which in some cases may encompass the entirety of the document.
The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”
The phrase “and/or.” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B,” when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.
As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example. “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.
In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03. It should be appreciated that embodiments described in this document using an open-ended transitional phrase (e.g., “comprising”) are also contemplated, in alternative embodiments, as “consisting of” and “consisting essentially of” the feature described by the open-ended transitional phrase. For example, if the disclosure describes “a composition comprising A and B,” the disclosure also contemplates the alternative embodiments “a composition consisting of A and B” and “a composition consisting essentially of A and B.”
This application is a national stage filing under 35 U.S.C. 371 of International Patent Application Serial No. PCT/US2018/018547, filed Feb. 17, 2018, which claims priority under 35 U.S.C § 119(e) to U.S. provisional application No. 62/460,121, filed Feb. 17, 2017, the contents of each of which is incorporated herein by reference in its entirety.
This invention was made with Government support under Grant Nos. HDTRA1-15-1-0051 and HR0011-15-C-0091 awarded by the Defense Threat Reduction Agency, and Grant No. MCB-1350949 awarded by the National Science Foundation. The Government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2018/018547 | 2/17/2018 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 20200199568 A1 | Jun 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62460121 | Feb 2017 | US |
