NODES is a mutation-specific high-throughput glioma drug screening platform. It integrates a non-invasive reporter system measuring D-2-HG levels in patient-derived glioma cells to characterize drug responses specific to the common brain cancer IDH1 mutation. NODES further optimizes the scalability of organoid production through the application of microfluidics to create minibrains, or mini brain organoids. By combining reporter-integrated glioma cells with minibrains, our system optimizes drug screening of patient-derived glioma cells and facilitates developments in precision medicine.

We decided on a two-phase project because developing a cohesive drug screening platform encompasses a multitude of nuanced components. We are applying synthetic biology in novel ways to integrate genetic circuits into patient-derived glioma cells. In addition, growing organoids is a long and arduous process. Since we are exploring a new application of synthetic biology, we believe that a two-phase project will allow us to fully maximize the potential and impact of NODES. Our two-phase project plan is detailed below:

Figure 1. Duke iGEM two-phase project outline

Emphasis on IDH1 mutation


Current drug development pipelines rely on animal testing for safety and efficacy screening, but this process is costly, time-consuming, and often disappointing. More than $18 billion is spent on animal drug screening experiments every year, only for many drugs to show poor efficacy in animals (Tonkens et al., 2005). There is currently no way around this dilemma as animal testing is the current standard for validating novel treatments.

With this in mind, we began brainstorming potential testing models. Initially, we wanted to create a platform that could measure therapeutic efficiency across all forms of gliomas. However, generalized models tend to become less effective due to the complex nature of this disease.

Additionally, a generalized model is not ideal, as complications would arise during the design and implementation process, preventing the collection of consistent and reliable results when screening over multiple glioma variants. This, along with stakeholder interviews and literature analysis, motivated us to develop a mutation-specific reporter system that could model a specific pathway or measure changes of a specific target molecule. A mutation-specific system would allow users to observe consistent and reliable data from every sample.

Specifically, we decided to focus on the isocitrate dehydrogenase 1 mutation. IDH1R132H (IDH1) is frequently detected in glioma cases and has well-defined impacts on important metabolic processes that have crucial implications for patient outcomes (Han et al, 2020). Given that IDH1 has well-established downstream impacts, focusing our design around it would allow us to consider how treatments impact cancer development from several angles. The IDH1 mutation leads to the upregulation of D-2-hydroxyglutarate (D-2-HG), an oncometabolite that causes signaling dysregulation upon accumulation. The upregulation of D-2-HG in IDH1 mutations is the foundation of our reporter system, which uses the allosteric transcription factor DhdR and fluorescent proteins to monitor the levels of 2-HG in our co-culture system. Further motivation for focusing on the IDH1 mutation came from the fact that IDH1 mutant cell lines are considered difficult to establish in animal models (Luchman et al., 2012). By utilizing a co-culture system with mutation-specific cells, we hope that our proposed system will allow researchers to overcome these limitations and facilitate the process of meaningful drug discovery (Figure 1).

Figure 2. The two major motivations behind designing an IDH1 mutation-specific model.

Targeting D-2-HG molecule

Our goal is to create a model that allows drug development researchers to gain a better understanding of the exact molecular and metabolic impacts of their therapeutics on disease samples. After choosing to target IDH1 mutation-specific gliomas, we focused on quantifying D-2-HG concentrations in the cell. Due to the downstream effects of D-2-HG accumulation, which include genome-wide histone and DNA methylation alterations, monitoring its levels within the system and observing how those levels change in response to novel therapeutics is crucial to find a cure for this lethal disease.

Reporter System Design

DhdR and dhdO Plasmid Design

Our reporter system is made up of two components: 1) a pcDNA5 (Thermo Fisher, V103320) plasmid we modified to include the DhdR gene that produces the allosteric transcription factor and 2) a fluorescent (tdTomato, mCherry) or luminescent (cLuc) reporter plasmid that allows users to non-invasively measure D-2-HG levels via DhdR derepression (Figure 2). The reporter plasmid contains a promoter (CMV or hUbC), the dhdO binding site, and a fluorescent or luminescent gene.

Figure 3. Plasmid maps of the DhdR plasmid constructs (left) and the constitutive reporter constructs (right).

We outline the design process of these plasmids below:

  1. While researching potential biosensors for D-2-HG, we found a paper by Xiao et al. outlining the discovery of an allosteric transcription factor, DhdR, from Achromobacter denitrificans NBRC 15125, which negatively regulates D-2-HG dehydrogenase expression in response to D-2-HG. This article was the foundation of our reporter system. We ordered the human-codon optimized DhdR gene from Twist Bioscience, and inserted it into a commercially available pcDNA5 plasmid (Thermo Fischer, V103320) to serve as our biosensor.
  2. Our reporter constructs consist of dhdO binding sites and a fluorescent (tdTomato, mCherry) or luminescent (cLuc) reporter gene. (Xiao et al. included a series of DhdO binding sites sequences and their KD dissociation constants, which reflect their binding affinities to DhdR. The authors tested 14 different binding site sequences, but our team decided to focus on the two with the lowest KD, dhdO 0# and dhdO 5# (Figure 3). After choosing these two binding site sequences, we designed ten binding site combinations with varying numbers of repeats and spacer sequences (Figure 4), which were all obtained from Integrated DNA Technology. The motivation behind this was to optimize the binding of our system, with the hopes of creating a tunable reporter or a cooperative binding that enhances reporter activity. The presence of binding sites and variations in the number of repeats can impact the binding behavior of associated proteins (Cambridge, 2014). Thus, our team hopes to test the ten binding site combinations to determine which variation offers the largest dynamic range of expressions, as this would allow our system to effectively serve as a biosensor for the compound of interest, D-2-HG.

Figure 4. Optimization of the D-2-HG biosensor from “A D-2-hydroxyglutarate biosensor based on specific transcriptional regulator DhdR” (Xiao et al., 2021)

Figure 5. A visual representation of the ten binding site variations (BS 1-10) our team designed. Each segment varies by the number of dhdO binding site repeats and spacer sequences, which we will test to determine the optimal number and position of each binding site.

Expected Outcomes

In a wild-type environment, without the presence of DhdR, we expect normal expression of the fluorescent or luminescent protein. However, when DhdR is present, it will bind to the dhdO binding site, allosterically blocking the transcription of our reporter gene. When D-2-HG is elevated, as observed in IDH1 mutant cells, it binds to DhdR, releasing it from the dhdO binding site. This allows for transcription of the downstream reporter protein sequence, resulting in brighter expression that is visible in our in vivo droplet system (Figure 5). Since D-2-HG levels are elevated due to the IDH1 mutation, we expect an increase in fluorescence or luminescence due to the release of the DhdR caused by the binding of the upregulated oncometabolite. When we perform drug screening assays on our completed co-culture system, we will associate decreased fluorescence or luminescence with lower levels of D-2-HG, corresponding to reduced tumor growth.


Figure 6. This figure outlines the mechanism of the interaction between the DhdR allosteric transcription factor and the dhdO binding site. The top of the figure outlines normal expression of the reporter sequence, resulting in fluorescing cells. Case 1 is the wildtype condition with no D-2-HG in the system. No fluorescence is expected in this case. Case 2 is the IDH1 mutant condition, when D-2-HG is upregulated. We expect fluorescence expression because of the interaction between D-2-HG and DhdR.  

Fluorescent and Luminescent Plasmid Design

To design our reporter plasmid, we first had to select a reporter that allows users to non-invasively visualize changes in cell behavior, such as using fluorescence or luminescence.  We decided on tdTomato and mCherry, two well-known red fluorescent proteins, and Cypridina Luciferase (cLuc), a bioluminescent reporter found naturally in the ostracod Cypridina noctiluca. tdTomato is a brighter version of current red fluorescent proteins, such as DsRed-Monomer, and as such is ideal for imaging (Shaner, et. al). mCherry was used as it was already included in the well-established pcDNA5 plasmid backbone that we used as the base for our cloning. cLuc is unique in that it is secreted from mammalian cells, so we can measure its expression in the media. Since each of these reporter proteins are noninvasive, we are able to measure time courses of living co-culture organoids instead of performing end-point assays and completely disrupting the system.

In our constructs, we used two different promoters: CMV and hUBC. The CMV promoter is derived from the cytomegalovirus and the hUbC promoter is the human promoter from the ubiquitin C gene; both of these promoters are well characterized, exhibit high constitutive expression in mammalian cells, and have been used extensively in synthetic biology work (Mortiz, 2015; Schorpp et. al, 1996). By testing these two different promoters, we hope to identify the construct combinations that give the largest dynamic range, allowing our system to precisely report D-2-HG levels over a large range of concentrations.

Patient tumor transfection  

Our plasmid reporter system can be transfected into primary glioma cells to assess how patient-derived cells respond to different therapeutics. Although non-viral transfection techniques are commonly used to insert plasmid DNA into common cell lines such as HEK 293T cells, plasmid transfections of glioma cells have not been well studied. Our goal is to optimize a protocol for transferring our plasmids into glioma cells, resulting in high transfection efficiency and steady gene expression. See Proof of Concept to learn more about our optimization design process.

Developing a High-Throughput Organoid Platform

Traditional Method of Generating Organoids  

Traditional methods for generating mini brains are time-consuming and labor-intensive, with a low throughput production of organoids. Usually, a 96-well plate is used and a specific number of stem cells must be seeded into each well. The stem cells self-aggregate into embryoid bodies and differentiate over several stages, during which all of the cell aggregates need to be handled separately with meticulous micropipetting skills. On day 11 of organoid development, high quality embryoid bodies need to be manually selected from the cohort and encapsulated in an independent droplet of an extracellular matrix (ECM), namely Matrigel, for the maturation stage. During maturation, an orbital shaker or bioreactor is required for successful organoid development. Clearly, handling cerebral organoids is a long and tedious process and must be improved (Figure 6).

Figure 7. A timeline schematics of the traditional method of generating cerebral organoids. Note that day 11 sees the incorporation of embryoid bodies into Matrigel droplets, and a bioreactor is used from day 15 forward (usually until day 50+ when the organoids mature).

Novel Droplet-based Method to Increase Throughput

Figure 8. The timeline of the droplet-based mini brain workflow

Microfluidics is an emerging field of technology that enhances the ability to handle very small volumes of fluid in an automatic and high-throughput manner. We capitalized on this new technology by applying it to the early stages of organoid development, allowing us to overcome any previous barriers that hindered the mass generation of embryoid bodies. By using microfluidics to produce organoids encapsulated in droplets, we are able to improve upon traditional methods of generating cerebral organoids. These droplets significantly enhance the minibrain production rate, allowing for larger batch sizes and lower overall maintenance. This enables the development of high-throughput drug screening platforms that can test a multitude of drugs with the same cohort of cerebral organoids co-cultured with patient-derived glioma cell lines to reduce batch variability. Previously existing protocols handle each organoid individually, which increases the likelihood of gross errors and experimental variations.

Applying droplet-based technology to organoid development allows for the rapid generation of hundreds to thousands of embryoid bodies that are standardized in size and mitigates handling errors (Figure 7). Droplet-based minibrains potentially remove the need for a bioreactor or an orbital shaker during the maturation stage of the organoids. Further, since the current cerebral organoid differentiation process heavily depends on the self-patterning of cell clusters, applying this technology can ensure consistency within one batch of minibrains by generating droplets containing small cell aggregates of a certain cell number. With the abovementioned advantages of the microfluidics system, we decided that we would like to incorporate this technology into our design of a high-throughput drug screening platform.

Tumor-minibrain co-culture platform for drug screening

Our proposed model relies on the use of a co-culture system, which will be established using the timeline shown in Figure 8. This allows us to take advantage of the many benefits related to organoid use, particularly the ability to mimic the tumor microenvironment. In addition, because co-culture systems rely on the growth of cancer cells and organoids in the same environment, this setup permits the development of tumor-tissue interactions that would naturally occur in vivo (Chekhonin et al., 2018). In this manner, these systems extend the benefits offered by organoids, allowing for accurate recapitulation of tumor and organ characteristics within an in vitro setting. In order to establish our co-culture system, we will utilize a two-track process, outlined in the figure below (Figure 8). Through droplet generation, minibrains will be generated and matured over the course of 40+ days. At the same time, patient samples would be collected and transfected with our noninvasive reporter system. After this phase, the cells would be co-cultured with the minibrains, after which a drug assay and corresponding analysis will be run on the combined system

Figure 9. Schematic of the two-track process for establishing the co-culture system utilized by NODES

Current drug testing relies heavily on patient-derived xenograft (PDX) models, which presents a series of challenges: it is expensive and extremely difficult to develop, requires a complex protocol to implement, and does not accurately and reliably model certain fundamental aspects of human diseases, as rodent brain structures significantly differ from human brains. On the other hand, stem cell derived cerebral organoids recapitulate the human brain microenvironment, and therefore can provide useful insight on how cancer cells can interact with normal brain cells in vitro. A drug-screening platform based on an in vitro organoid model holds tremendous predictive power of the drug efficacy in vivo, and can provide preliminary results for selecting a series of novel compounds to move forward in the drug development pipeline.


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