Project Description page few iGEM teams have chosen to work on mammalian systems, despite their inherent potential. We conducted an IRB-approved survey of iGEM teams and their advisors - the results of which are summarized on our Increasing Accessibility page - to assess what was keeping iGEM teams from working on mammalian systems, particularly those teams that had expressed interest early on to work on mammalian systems but ultimately decided against it. These activities helped to identify what problems we chose to work on and how we structured our work over the summer.
Problem Identification and Design Specifications
We considered numerous ways in which we might contribute to the development of mammalian synthetic biology within iGEM. We started off interested in characterizing promoters to increase the number of parts within the mammalian registry, so we began researching methods of gene regulation sorting them into the five main methods shown in the flow chart on the right (cell signaling, temperature, light, pH, and small molecules). A more in-depth walk through of our decision making process can be found on the Project Description page.
In the end, we focused our efforts on working to characterize the activity of engineerable gene regulators, core parts of almost any synthetic genetic system. We focused on specifically on characterizing the Light Activated CRISPR-dCas9 Effector (LACE) system [1]. This system is particularly interesting in the context of mammalian synthetic biology since it allows bioengineers to design both positive and negative regulators of gene expression that can be controlled using light in both time and space - important in multicellular structures.
Our project’s engineering design specifications included satisfactorily completing the requirements for an iGEM Gold Medal, many of which already require us to create a new part and characterize it properly. However, we also wanted to make those parts and the methods we developed to characterize the parts accessible to others. Our foundational advances in Measurement and Modeling are described on the relevant pages.
Standardization and Measurement
Our project sought to characterize the transfer function of LACE systems designed to regulate the expression of specific endogenous genes. To make these characterizations useful to others, we needed to propose the development of new standards (variables, methods, models, and units) for measuring the functional relationship between light input and mRNA output. For instance, we needed to consider several contextual variables that affect these systems including the selection of cell lines, target genes, and sgRNAs.
In addition, light system specific variables like duration, intensity, and flashing frequency needed to be accounted for as they affect switching kinetics and active state of the light-inducible element. These factors have not been well documented in the literature.
In optogenetics work, the method of generating and characterizing the light “inducer” has not been standardized between labs, which makes it hard to compare data across studies. To begin rallying around a common platform, in this study, we decided to utilize and improve upon a previously published light source and to test the suitability of low-cost and easily accessible tools for measuring light intensity for calibration [2,3]. We also propose and demonstrate the importance of measuring sample heating induced by the illumination platform as a standard characterization of an optogenetic light source, documented in the Hardware page.
In designing our sgRNAs for our construct and qPCR primers that probe for our target genes, we standardized the length to about 20bps, annealing temperature, and binding location such that we can use the same thermocycler settings. Several quality control measures were implemented to validate qPCR probes. Visit Experiments to see the design considerations and Parts to see our final sequences and characterization.
In all systems, the first step to getting a genetic device to function in the cell is to get the parts get into the chassis. Rapid prototyping of devices in mammalian systems requires the use of transient transfection. Unlike work in bacterial systems, the transfection efficiency vary between cell lines and is an important variable that needs to be accounted for in the quantitative characterization of part/system behavior. Transfection efficiency is, however, difficult to measure. We expanded on several methods for measuring transfection efficiency based on fluorescent imaging and/or flow cytometry to be considered as standard methods. The imaging and flow protocols can be found on our Experiments page. Efficiency was included in our modeling work along with a variable for dilution of transfected plasmids through cell division, which we propose should be included as a standard variable in models of systems created through transient transfection. See the Modeling page for more information.
Responsible Use
All iGEM projects need to integrate human practices into their plans. Integrating human practices can mean a variety of things including but not limited to justifying why a team chooses to spend their time and resources on a project, the motivation for pursuing a project, and the careful weighing of potential benefits and risks if the project is successful (who will use the product, how and what impacts these uses might have). As noted on their respective pages, we considered Safetyand the importance of Ethics from the beginning to ensure that we were doing research responsibly. We also consulted with a bioethicist and worked to create a new Ethic and Community Impact Assessment (ECIA). This Public Engagement was primarily targeted at undergraduate researchers participating in or proposing projects in the UC Davis BioInnovation Group, but they could also be used elsewhere.
Project Management
A broad overview of our project organization can be found in the critical path chart below. The project was deliberately structured so that many elements of our work could be conducted in parallel.
Design Iterations
Our goal to engineer and characterize light-inducible gene regulatory system required us to compare various different optogenetic systems. We compared systems on activation wavelength, activation rate and penetration depth. We discovered that red wavelengths of light were more deeply penetrating into tissues and less toxic than blue or UV light but had fewer characterized systems described in the literature. Red systems also seemed to require more cofactors [4]. Despite this fact, optogenetic far-red light (FRL)-activated CRISPR-dCas9 system (FACE) system in tangent with the blue Light Activated CRISPR-dCas9 Effector (LACE) system had potential [5].
After considering the limitations of the iGEM time interval, we eventually decided that it would be better to focus on a single system and conduct a more detailed characterization than a surface level experiment with both, so we selected the system with fewer cofactors and a more logical design: the LACE system.
We outlined experiments for characterization within three mammalian cell lines and the generation of dose response curves and system efficiency at short (5hr), medium (24hr), and long(72hr) time intervals. We designed and ordered sgRNAs for a range of endogenous genes, housekeeping reference genes, and qPCR primers for the testing of each.
During our early trials, we were not able to get as much output from our 3 plasmid control system targeting an exogenous GFP. Based on this, we redirected our focus to troubleshooting how much of our plasmids got into the cells and whether the sgRNAs are binding to the endogenous DNA.
See our Experimental Testing Plan below for more information.
Experimental Testing Plan
The goal of our experiment to characterize the Light Activated CRISPR-dCas9 Effector (LACE) system for upregulation of endogenous genes within mammalian cells resulted in a multi-stage project. The general steps can be sorted into the following categories.
Part design and construction
Construction of the Light Box (LPA)
Optimization of transfection efficiency within three mammalian cell lines
qPCR characterization of the LACE system on endogenous gene activation
Part Comparison of LACE systems (NEU China 2016)
Part Design and Construction
The main form of data collection we propose for this experiment is the measurement of intracellular mRNA expression via qPCR with flow cytometry and fluorescence imaging to factor in transfection efficiency. Rather than using a plasmid with an indicator, such as GFP for our final system, we elected to regulate endogenous genes as they more accurately emanate future research applications of LACE.
Our two plasmid system uses a CRISPR-dCas9 targeting system with an sgRNA scaffold into which we cloned our specified guide RNAs for each endogenous gene. The blue light dependent dimerizing pair of CRY-2 and CIBN are attached to either an activator (VP64 or VPR) or a CRISPR-dCas9 targeting system with an sgRNA scaffold containing our designed guide RNAs. For details on the cloning process and designed parts see Parts and Experiments.
Endogenous Gene Selection
The genes we selected were chosen on multiple criteria, including minimal impact on intracellular processes (ie. metabolism or stress response), a relatively low basal expression level, and their presence within all three selected cell lines.
IL1RN, ASCL1, NANOG, and CXCR4 promoters have been targeted for activation via LACE system in HEK293T; therefore we chose to replicate their activation in CHO-DG44, AML-12, and NIH3T3 cell lines to broaden the existing knowledge base [1].
A common application of CRISPR-dCas9 based regulation systems is the triggering of iPSCs (induced pluripotent stem cells) into the differentiation of specific cell types [6]. COL2A1, SOX9, RUNX2 are all genes that have been shown to influence the differentiation of iPSCs into chondrocytes, providing a framework for discussion and cell-line comparison within this field [7].
Guide RNAs were designed to target endogenous genes within NIH-3T3, AML-12, and CHO-DG44 adherent, immortal, mammalian cell lines using BLAST.
Targeted Endogenous Genes
Gene
Full Name
Function
IL1RN [8]
Interleukin 1 Receptor Antagonist
Inhibits the activity of interleukin-1, IL1A, and IL1B. It modulates interleukin 1 related immune and inflammatory responses.
ASCL1 [9]
Achaete-Scute Family BHLH Transcription Factor 1
It plays a role in neuronal commitment and differentiation and in the generation of olfactory and autonomic neurons.
NANOG [10]
Nanog Homeobox
Involved in embryonic stem cell proliferation, renewal, and pluripotency.
CXCR4 [11]
C-X-C Motif Chemokine Receptor 4
Encodes for a CXC chemokine receptor specific for stromal cell-derived factor-1. With 7 transmembrane regions, it works with CD4 to support HIV entry into cells. This receptor is also highly expressed in breast cancer cells.
COL2A1 [12]
Collagen Type II Alpha 1 Chain
One component of type II collagen, which adds structure and strength to connective tissues.
SOX9 [13]
SRY-box 9
Plays a critical role during embryonic development - development of the skeleton and sex determination before birth.
RUNX2 [14]
Runt Related Transcription Factor 2
As a transcription factor, it is one of the master switches that regulates genes involved in teeth, bones, and cartilage.
During the summer we were only able to successfully complete an IL1RN endogenous targeting system but parts were designed, synthesized, and begun for the others on the list as well. We plan to continue work on this system after the Jamboree.
Reference Gene Selection
In order to differentiate between the effects of basal gene expression, transfection efficiency, and transfection dependent cellular stress from those of our designed lace system, we selected a set of eight reference genes.
1. Basal gene expression
Each endogenous gene has a varied basal mRNA expression level, so an unactivated “before” state is required to serve as comparison with the post-LACE activation mRNA result. The normalization of this scale will provide data in the form of a fold increase of expression.
These values were obtained using qPCR primers for the endogenous target genes designed above in an untransfected sample and a sample transfected but left in the dark as an “off” system and detect any system leakiness.
Due to the strict time restraints of the summer, our experiment focused on the effects of IL1RN and some experiments with CXCR4. See Experiments for more info.
2. Intracellular Stress Response
The act of transfection and growth in blue light conditions may lead to slight variations in intracellular conditions that also affects general transcription and translation of the genome as a whole. To track this possible change, we selected four separate endogenous genes found in all three cell lines to track via mRNA expression. Previous studies revealed a list of several housekeeping genes commonly used as reference in qPCR for this purpose, and we selected GADPH, Rn18s, B2M, ACTB [15].
Primer verification against genomic DNA from all three cell lines led to some off target amplification from GADPH and ACTB, leading to the majority use of RN18s and B2M for our experiments. See Experiments for more info.
2. Transfection Efficiency
A control we found largely lacking from literature was transfection efficiency when working with endogenous genes. Transfection within mammalian cells is notoriously difficult and leads to large variability from cell-to-cell, as well as sample-to-sample based on slight fluctuations on technique and conditions.
To account for possible transfectional variation we selected four reference genes from our own plasmids (dCas9, CibN, CRY2, VP64), two from each vector. The qPCR data from these genes will indicate average plasmid number across the cell population and allow us to differentiate the effects of transfection efficiency from that of our LACE system. These values were also verified using CY3 and CY5 plasmid dye and flow cytometry.
Primer verification against genomic DNA from all three cell lines and plasmid reference vectors led to irregular off target amplification from most of our designed plasmid qPCR primers, but re-designed and re-ordered versions of dCas9 and CRY2 were used as reference in our experiments. See Experiments for more info.
Reference Gene Controls
Gene & Control Condition
Full Name
Function
GADPH [16] (intracellular Stress)
Glyceraldehyde-3-phosphate Dehydrogenase
A moonlighting protein that can perform mechanistically distinct functions.
Catalyzes an important energy-yielding step in carbohydrate metabolism
Have uracil DNA glycosylase activity in the nucleus
Contains a peptide that has antimicrobial activity against E. coli, P. aeruginosa, and C. albicans.
Rn18s [17] (intracellular stress)
18S Ribosomal RNA
Ribosomal subunit
B2M [18] (intracellular stress)
Beta-2-microglobulin
A component of MHC class I found on the surface of almost all nucleated cells. Involved in the presentation of antigens.
ACTB [19] (for intracellular stress)
Actin Beta
Found in cells throughout the body, they play important roles in determining cell shape and motility. May be involved in relaying chemical signals within cells.
dCas9 [20] (plasmid reference)
Catalytically dead Cas9
Cas9 mutant without endonuclease activity binds at site indicated by sgRNAs (guide RNAs) but does not cut.
CibN [21] (plasmid reference)
Cryptochrome-interacting basic-helix-loop-helix 1
Blue light activated dimerizing protein derived from Arabadopsis thaliana. Dimerizes with CRY2.
CRY2 [22] (plasmid reference)
Cryptochrome Circadian Regulator 2
Encodes for a flavin adenine dinucleotide-binding protein, a key component of the circadian core oscillator complex.
VP64 [20] (plasmid reference)
Activation domain
Activation domain containing 4 copies of VP16, viral protein sequence.
VPR [23] (plasmid reference)
Activation domain
VP64-p65-Rta
Primer verification against extracted genomic DNA led to some off target hits for some qPCR primers, and due to the desire to use the same reference genes throughout all experiments to maintain comparability, we did not end up using all of the above.
Construction of the Light Box (LPA)
With the use of an open source design provided by all the collaborators of Tabor Lab at Rice University, we built a specialized light box to hold 24-well plates [2,3]. Documentation of our final Bill of Materials, manufacturing, and assembly can be found on the Hardware page.
The Light Plate Apparatus (LPA) allows modulation of intensity and frequency to test the effects of concentrated light on cultured cells in a controlled environment. For our project design, we utilized blue light with a wavelength of (472 nm) to test our LACE system since the CRY2-CIBN is receptive to blue light.
Testing of the light box includes calibration of each well using a LUX meter to ensure each replicate receives an equivalent amount of light, and a temperature test to experimentally determine the optimal light range to activate the system without overheating the cells. See Hardware for details.
Optimization of Transfection Efficiency
Due to the multi-plasmid design of our LACE targeting system, it is necessary to experimentally optimize transfection conditions to increase expression levels within the different cell lines. The number of plasmids, characteristics of the cell line, and a range of other experimental conditions greatly influence the amount of plasmid that successfully enters your cell population.
We devised a series of tests that can be conducted to optimize most chemical transfection methods into adherent mammalian cell lines for future iGEM teams using both a single plasmid constitutive GFP vector and a model three plasmid system to better represent our final experiment endogenous gene two plasmid system. For information on each test and detailed protocols see Experiments.
Simplified model of three plasmid system
Simplified model of two plasmid system
qPCR System Characterization Accommodating for Transfection Efficiency
In order to fully characterize the effectiveness of our optogenetic LACE system we conducted both RNA and DNA extractions on transfected wells followed by reverse transcription into cDNA for the RNA and qPCR on both. This data was normalized against housekeeping endogenous reference genes and the basal expression of the gene of interest from untransfected cells. Transfection efficiency was based on flow cytometry, imaging, and qPCR of plasmid reference genes to determine average plasmid number within the transfected cellular population.
Controls
In an effort to mitigate the possible effects of extraneous intracellular processes, off target effects, and external light sources to maximize the significance of our data, we planned a series of controls to include in each test.
Randomization of Samples
The light boxes, as we designed them, are made to block out as much external light as possible through the black 3D printed sides, and black plastic of our 24 well plates. However, slight variations within the manufacturing of the light bulbs, or position of the cells in relation to the edge vs center of the box (edge effects) may lead to variations of intensity and leakage. To accommodate for these variations we randomized the positions of the cells during each experiment.
Red Light Control
In order to determine if the LACE system is activated by other wavelengths of light, we replaced the blue LEDs in our Light Box with red. This additionally proved that the red overhead light we used during cell extractions and manipulation did not accidentally influence expression levels.
LACE System Comparison
Variations of the LACE system exists both within literature and within the context of iGEM. In 2016, NEU China worked to characterize the effects of sgRNA placement and design on the overall effectiveness of the LACE targeting system on a GFP plasmid vector. Systems 1 and 3 are an improvement on the NEU system.
System 1: Team Zorya UCD 2019 system
The two plasmids involved are pcDNA3.1-CRY2FL-VP64 and pX330A-1x3-CibN-dCas9-CibN.
Here, light sensitive CRY2 and the activator VP64 or VPR are connected and dCas9 is connected to light sensitive CibNs on both terminals. Three guide RNA sequences are present on the dCas9 containing plasmid. Our light activation and deactivation duration experiments uses this system.
System 2: NEU-CRY2-VP64 and NEU-tCas9-CibN
System 2 replicates the system used by NEU China in 2016. Light sensitive CRY2 and the activator VP64 are connected and tCas9 is connected to one light sensitive CibN. Systems 1 and 2 differ in the region that connects CRY2 and VP64 . In addition, System 2 contains one CibN while System 1 contains two. We expected that System 1 should have greater expression compared to System 2 since the presence of two fused CibNs to dCas9 has increased expression in the literature. [1]
System 3: VPR activation
This system includes the plasmids pcDNA3.1-CRY2FL-VPR and pX330A-1x3-CibN-dCas9-CibN. System 3 has the same CibN-dCas9-CibN as system 1. However, the activation domain connected to CRY2 is switched out with VPR instead of VP64.
References
[1]
Polstein, Lauren R, and Charles A Gersbach. “A Light-Inducible CRISPR-Cas9 System for Control of Endogenous Gene Activation.” Nature Chemical Biology, vol. 11, no. 3, 2015, pp. 198–200., doi:10.1038/nchembio.1753.
[2]
Gerhardt, Karl P., et al. “An Open-Hardware Platform for Optogenetics and Photobiology.” Nature, 2016, doi:10.1101/055053.
[3]
“The Light Plate Apparatus (LPA).” The Light Plate Apparatus (LPA) - Light Plate Apparatus 0.9 Documentation, http://taborlab.github.io/LPA-hardware/index.html.
[4]
“Platform on Molecular Optogenetics.” OptoBase, www.optobase.org/.
[5]
Shao, Jiawei, et al. “Synthetic Far-Red Light-Mediated CRISPR-dCas9 Device for Inducing Functional Neuronal Differentiation.” Proceedings of the National Academy of Sciences, vol. 115, no. 29, 2018, doi:10.1073/pnas.1802448115.
[6]
Mandegar, Mohammad A., et al. “CRISPR Interference Efficiently Induces Specific and Reversible Gene Silencing in Human IPSCs.” Cell Stem Cell, vol. 18, no. 4, 2016, pp. 541–553., doi:10.1016/j.stem.2016.01.022.
[7]
Suchorska, Wiktoria Maria, et al. “Gene Expression Profile in Human Induced Pluripotent Stem Cells: Chondrogenic Differentiation in Vitro, Part A.” Molecular Medicine Reports, vol. 15, no. 5, 2017, pp. 2387–2401., doi:10.3892/mmr.2017.6334.
[8]
“Cricetulus Griseus Interleukin 1 Receptor Antagonist (Il1rn - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/nucleotide/1537967006.
[9]
“Cricetulus Griseus Achaete-Scute Family BHLH Transcription.” National Center for Biotechnology Information, U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/nucleotide/1537929195.
[10]
“Cricetulus Griseus Nanog Homeobox (Nanog), Transcript Varia - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/nucleotide/1032915306.
[11]
“Cricetulus Griseus C-X-C Motif Chemokine Receptor 4 (Cxcr4) - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/nucleotide/1537923451.
[12]
“Cricetulus Griseus Collagen Type II Alpha 1 Chain (Col2a1), - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/nucleotide/1537972198.
[13]
“Cricetulus Griseus SRY-Box 9 (Sox9), Transcript Variant X1, - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/nucleotide/1537918090.
[14]
“Cricetulus Griseus Runt Related Transcription Factor 2 (Run - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, https://www.ncbi.nlm.nih.gov/nucleotide/1537980516.
[15]
Kozera, Bartłomiej, and Marcin Rapacz. “Reference genes in real-time PCR.” Journal of applied genetics vol. 54,4 (2013): 391-406. doi:10.1007/s13353-013-0173-x
[16]
“Cricetulus Griseus Glyceraldehyde-3-Phosphate Dehydrogenase (Gapdh), m - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, https://www.ncbi.nlm.nih.gov/nuccore/NM_001244854.2.
[17]
“Cricetulus Griseus Actin Beta (Actb), Transcript Variant X1 - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, https://www.ncbi.nlm.nih.gov/nuccore/XM_007648665.3.
[18]
“Rn18s 18S Ribosomal RNA [Mus Musculus (House Mouse)] - Gene - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, https://www.ncbi.nlm.nih.gov/gene/19791.
[19]
“Cricetulus Griseus Beta-2-Microglobulin (B2m), MRNA - Nucleotide - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, https://www.ncbi.nlm.nih.gov/nuccore/NM_001246674.2.
[20]
Xu, Xiaoshu, and Lei S Qi. “A CRISPR-DCas Toolbox for Genetic Engineering and Synthetic Biology.” Journal of Molecular Biology, U.S. National Library of Medicine, 4 Jan. 2019, https://www.ncbi.nlm.nih.gov/pubmed/29958882.
[21]
“CIB1 Cryptochrome-Interacting Basic-Helix-Loop-Helix 1 [Arabidopsis Thaliana (Thale Cress)] - Gene - NCBI.” National Center for Biotechnology Information, U.S. National Library of Medicine, https://www.ncbi.nlm.nih.gov/gene/829604.
[22]
“CRY2 Gene - Genetics Home Reference - NIH.” U.S. National Library of Medicine, National Institutes of Health, https://ghr.nlm.nih.gov/gene/CRY2.
[23]
Chavez, Alejandro, et al. “Highly Efficient Cas9-Mediated Transcriptional Programming.” Nature Methods, U.S. National Library of Medicine, Apr. 2015, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393883/.
We considered numerous ways in which we might contribute to the development of mammalian synthetic biology within iGEM. We started off interested in characterizing promoters to increase the number of parts within the mammalian registry, so we began researching methods of gene regulation sorting them into the five main methods shown in the flow chart on the right (cell signaling, temperature, light, pH, and small molecules). A more in-depth walk through of our decision making process can be found on the Project Description page. 
With the use of an open source design provided by all the collaborators of 
The light boxes, as we designed them, are made to block out as much external light as possible through the black 3D printed sides, and black plastic of our 24 well plates. However, slight variations within the manufacturing of the light bulbs, or position of the cells in relation to the edge vs center of the box (edge effects) may lead to variations of intensity and leakage. To accommodate for these variations we randomized the positions of the cells during each experiment.