Reliably controlling light-responsive mammalian cells requires the standardization of optogenetic methods. Toward this goal, this study outlines a pipeline of gene circuit construction, cell engineering, optogenetic equipment operation, and verification assays to standardize the study of light-induced gene expression using a negative-feedback optogenetic gene circuit as a case study.
Reliable gene expression control in mammalian cells requires tools with high fold change, low noise, and determined input-to-output transfer functions, regardless of the method used. Toward this goal, optogenetic gene expression systems have gained much attention over the past decade for spatiotemporal control of protein levels in mammalian cells. However, most existing circuits controlling light-induced gene expression vary in architecture, are expressed from plasmids, and utilize variable optogenetic equipment, creating a need to explore characterization and standardization of optogenetic components in stable cell lines. Here, the study provides an experimental pipeline of reliable gene circuit construction, integration, and characterization for controlling light-inducible gene expression in mammalian cells, using a negative feedback optogenetic circuit as a case example. The protocols also illustrate how standardizing optogenetic equipment and light regimes can reliably reveal gene circuit features such as gene expression noise and protein expression magnitude. Lastly, this paper may be of use for laboratories unfamiliar with optogenetics who wish to adopt such technology. The pipeline described here should apply for other optogenetic circuits in mammalian cells, allowing for more reliable, detailed characterization and control of gene expression at the transcriptional, proteomic, and ultimately phenotypic level in mammalian cells.
Similar to other engineering disciplines, synthetic biology aims to standardize protocols, allowing tools with highly reproducible functions to be utilized for exploring questions relevant to biological systems1,2. One domain in synthetic biology where many control systems have been built is the area of gene expression regulation3,4. Gene expression control can target both protein levels and variability (noise or coefficient of variation, CV = σ/µ, measured as the standard deviation over the mean), which are crucial cellular characteristics due to their roles in physiological and pathological cellular states5,6,7,8. Many synthetic systems that can control protein levels and noise4,9,10,11,12 have been engineered, creating opportunities to standardize protocols across tools.
One novel set of tools that can control gene networks that has recently emerged is optogenetics, enabling the use of light to control gene expression13,14,15,16,17. Similar to their chemical predecessors, optogenetic gene circuits can be introduced into any cell type, ranging from bacteria to mammals, allowing expression of any downstream gene of interest18,19. However, due to the rapid generation of novel optogenetic tools, many systems have emerged that vary in genetic circuit architecture, mechanism of expression (e.g., plasmid-based vs. viral integration), and light supplying control equipment11,16,20,21,22,23,24,25. Therefore, this leaves room for standardization of optogenetic features such as gene circuit construction and optimization, method of system utilization (e.g., integration vs. transient expression), experimental tools used for induction, and analysis of results.
To make progress on standardizing optogenetic protocols in mammalian cells, this protocol describes an experimental pipeline to engineer optogenetic systems in mammalian cells using a negative feedback (NF) gene circuit integrated into HEK293 cells (human embryonic kidney cell line) as an example. NF is an ideal system to demonstrate standardization since it is highly abundant26,27,28 in nature, allowing protein levels to be tuned and noise minimization to occur. In brief, NF allows for precise gene expression control by a repressor reducing its own expression sufficiently fast, thereby limiting any change away from a steady state. The steady state can be altered by an inducer that inactivates or eliminates the repressor to allow for more protein production until a new steady state is reached for each inducer concentration. Recently, an engineered NF optogenetic system was created that can produce a wide-dynamic response of gene expression, maintain low noise, and respond to light stimuli allowing the potential for spatial gene expression control11. These tools, known as light-inducible tuners (LITers), were inspired by earlier systems that allowed gene expression control in living cells4,10,29,30 and were stably integrated into human cell lines to ensure long-term gene expression control.
Here, using the LITer as an example, a protocol is outlined for creating light-responsive gene circuits, inducing gene expression with a Light Plate Apparatus (LPA, an optogenetic induction hardware)31, and analyze responses of the engineered, optogenetically-controllable cell lines to custom light stimuli. This protocol allows users to utilize the LITer tools for any functional gene they wish to explore. It can also be adapted for other optogenetic systems with diverse circuit architectures (e.g., positive feedback, negative regulation, etc.) via integrating the methods and optogenetic equipment outlined below. Similar to other synthetic biology protocols, the video recordings and optogenetic protocols outlined here can be applied in single-cell studies in diverse areas, including but not limited to cancer biology, embryonic development, and tissue differentiation.
1. Gene circuit design
2. Stable cell line engineering
3. Light plate apparatus induction assays
4. Fluorescence microscopy of light-induced engineered cells
5. Flow cytometry of light-induced engineered cells
6. RNA extraction and quantitative PCR of gene circuit components
7. Immunofluorescence of gene circuit components
Gene circuit assembly and stable cell line generation within this article were based on commercial, modified HEK-293 cells containing a transcriptionally active, single stable FRT site (Figure 1). The gene circuits were constructed into vectors that had FRT sites within the plasmid, allowing for the Flp-FRT integration into the HEK-293 cell genome. This approach is not limited to Flp-In cells, as FRT sites can be added to any cell line of interest anywhere in the genome using DNA editing technology such as CRISPR/Cas950.
Once appropriate cell lines were constructed and validated for the correct insertion, the LPA and IRIS software were chosen as a standardized protocol for light induction of gene expression31,51. The LPA system allows 24-wells to be programmed for induction of of mammalian cells in multiple experimental conditions depending on the light intensity, pulse duration, and duty cycle (Figure 2). Within this article, spatially uniform light intensity, pulse duration, and duty cycle were prioritized. However, researchers can use the IRIS software and the LPA for more advanced programming of temporal light wave patterns.
A crucial aspect about the LPA to be addressed before starting experiments is the optical calibration which can be performed for single or multiple devices. The previously described image analysis method31 used for calibration requires an image of the entire LPA with all LEDs of interest turned on, which can be acquired from a variety of sources52, including a gel imager. Here, a computer scanner was used for image acquisition (Figure 3A). Once the image was acquired, the open-source software created by Gerhardt et al. (2016)31 was used to calculate compensation values for each LED to ensure each LPA emits the same intensity at a given grayscale value. The software subtracts the background signal, thresholds the image into a binary category, and calculates the pixel intensity (Figure 3B). From the calibration, a minimal variation among the LEDs was found, with a CV of 0.04 between 96 wells (or 4 plates calibrated, Figure 3C). Lastly, using the calibration software demonstrated the LED variation by location (Figure 3D) and created a grayscale adjustment (Figure 3E) so that each LED is normalized to the same intensity.
In addition to calibration, other important aspects of using the LPA include integrating several controls in the experimental pipeline which can minimize systemic errors by limiting confounding variables such as light-toxicity effects and design limitations based on experimental materials. For example, Figure 4 illustrates two different configurations for programming different light intensities in the LPA wells. The first subpanel of Figure 4A shows an ascending organization of light intensity. This can make for ease of programming and data analysis but can produce edge effects where the bottom row of the LPA has the highest light intensity and can potentially cause unwanted heat and light induction of nearby wells. Similarly, in the second subpanel of Figure 4A, a randomization matrix has been applied to the IRIS software, creating various light intensities in random positions. This can minimize systemic influences of edge effects. A descrambled matrix (Figure 4B) is created with the IRIS software, which can then be utilized to determine the experimental conditions after the completion of the experiment and during analysis. Similar to the randomization of wells, users should also be aware of light toxicity effects that may occur (blue light within this work). In Figure 4C, two different light intensities used for gene circuit induction are shown with the same FSC-SSC gate based on the un-induced population of engineered cells. As such, users should perform a dose-response on the light modality of interest (e.g., pulse, duty cycle, etc.) to determine light toxicity effects that can occur at the high end of exposure (Figure 4D). Here, increasing blue light levels caused dose-dependent cellular debris, dead cells, and clumps compared to non-stimulated cells. As such, the continuous light intensity was restricted to around ¾ of the maximum LPA intensity (~3000 g.s. units).
Once proper equipment was set up, gene expression was then induced in engineered LITer gene circuits via fluorescence microscopy (Figure 5). Cells were induced at different light pulse durations on the LPA, which gave a light dose-response of gene expression at the population level. Cells within this work were imaged for GFP expression using 50 ms for FITC/GFP light source exposure time and for bright field imaging using 1-5 ms for phase-contrast exposure time. Given the robustness of this cell line-engineering protocol, any fluorescence marker could be used instead of GFP. The cells can be induced with multiple light regimes and produce a large range of responses (Figure 5). The latter is beneficial in further controlling the expression of functional genes (e.g., KRAS (G12V) oncogene in the original work53).
Immediately after fluorescence microscopy, cells could be analyzed in a variety of experimental protocols, including flow cytometry, qRT-PCR, and immunofluorescence. In this work, flow cytometry was performed as a first validation procedure and carried out following the methods outlined above (Figure 6). Similar to microscopy, cells were induced at different pulse length values, and showed a dose-response of gene expression of over four-fold change from un-induced states at the population level (Figure 6A–B). Similarly, gene expression noise reduction can be shown by comparing various light intensity values (Figure 6C–D) for the LITer system versus a positive regulation (no feedback) system such as the VVD/LightOn54. When comparing the LITer with the VVD system, negative feedback achieves over five-fold gene expression noise reduction. The same pre-induced cells imaged on microscopy could also be analyzed via qRT-PCR (Figure 7). Similar to flow cytometry, the engineered LITer cells could express RNA levels in a dose-responsive manner (over 10-fold induction from un-induced states), matching the dose-response of protein levels quantified by GFP expression on flow cytometry. The fluorescence microscopy and flow cytometry data mentioned can be calibrated using non-fluorescent cells, constitutively expressing fluorescent cells, and fluorescent 6-8-peak validation beads (e.g., FL1-channel green, fluorescent beads). Each of these components can allow the normalization of gene expression in a single experiment. However, these factors can also allow normalization of circuits and engineered cells across different experimental plates, experimental conditions, and days to allow comparison and standardization of gene expression data.
Lastly, the LITer gene circuits can be engineered to express functional genes, such as the KRAS (G12V, Figure 8). The versatility of this pipeline allows users to utilize the LITer architecture with cell engineering for any functional gene of interest, possibly incorporating additional architectures such as positive feedback. The LITer showed increasing amounts of blue light that resulted in increased levels of the gene circuit output (KRAS (G12V) protein levels) and, consequently, phosphorylated-ERK levels, both of which can be quantified via immunofluorescence assays.
Fragment | Ratio | Size (bp) | DNA fragment weight concentration (ng/μL) | DNA fragment molar concentration (fmol/μL) | Volume (μL) | Resulting DNA molar mass (fmol) |
Mother Vector fragment 1 | 1 | 3414.00 | 18.20 | 8.63 | 4.09 | 35.29 |
Mother Vector fragment 2 | 1 | 4642.00 | 18.10 | 6.31 | 5.59 | 35.29 |
Gene of Interest | 1 | 549.00 | 37.90 | 111.70 | 0.32 | 35.29 |
Total | 10.00 | 105.87 |
Table 1: DNA assembly reaction master mix calculation (step 1.13). Columns 2 & 3 are based on the size of the DNA fragments assembled and the concentration of these fragments after PCR amplification and agarose gel extraction (step 1.11). The bottom cell of the 4th column (total reaction volume) can be modified; however, the stated volume is recommended. Unshaded cells are generated automatically.
0 | 100 | 200 | 400 | 500 | 750 |
1500 | 3000 | 0 | 100 | 200 | 400 |
500 | 750 | 1500 | 3000 | 0 | 100 |
200 | 400 | 500 | 750 | 1500 | 3000 |
Table 2: IRIS programming of light intensities (g.s.) for light induction experiment in a 24-well plate with three replicates (step 3.4). Distribution of light intensities ranging from 0 to 3000 g.s. G.s.-grayscale units. This can be randomized by the IRIS software as needed.
Sample 1 | Sample 2 | Sample 3 | |
Reverse Transcriptase Master Mix Volume (μL) | 4.00 | 4.00 | 4.00 |
RNA Template (1000 ng) Volume (μL) | 2.00 | 3.00 | 4.00 |
NF-H20 Volume (μL) | 14.00 | 13.00 | 12.00 |
Total Volume (μL) | 20.00 | 20.00 | 20.00 |
Table 3: Reverse transcription master mix calculation (step 6.5). The reaction recommended total volume is 20 µL, and its components are the reverse transcriptase enzyme master mix (here: 5x), RNA template, and nuclease-free water. In this example, the RNA concentrations of samples 1, 2, and 3 are 500, 333, and 250 ng/µL, respectively.
Sample 1 | Sample 2 | Sample 1 & 2 Multiplexed | |
GFP Probe Volume (μL) | 1 | —– | 1 |
GAPDH Probe Volume (μL) | —— | 1 | 1 |
DNA polymerase Master Mix Volume (μL) | 10 | 10 | 10 |
cDNA (100 ng) Volume (μL) | 2 | 2 | 2 |
NF-H20 Volume (μL) | 7 | 7 | 7 |
Total Volume (μL) | 20 | 20 | 20 |
Table 4: Quantitative PCR master mix calculation (step 6.6). The reaction recommended total volume is 20 µL and its components are the DNA polymerase enzyme master mix (here: 2x), GFP and/or GAPDH probes, cDNA (100 ng total), and nuclease free water (NF-H20).
Figure 1: Circuit design and cell engineering. (A) Cell engineering tools, including LITer synthetic gene circuit with FRT integration sites, recombinase enzyme (Flp recombinase) for circuit integration, drug used for selection of appropriate genetic clones, and cell line of interest used for creating desired optogenetic mammalian cell lines. (B) Engineered genetic systems can be integrated at a specific FRT-site containing locus within the human genome. These genomic loci can then express specific antibiotic resistance or fluorescent reporter genes for selection of properly integrated genetic cassettes. Gene circuit integration can be initiated with the use of recombinases that recognize specific sites within the original genetic cassette. This introduces a novel drug resistance selection gene which can ensure generation of correctly engineered cells with the desired gene circuit. At the bottom of panel B is the gene circuit architecture for the optogenetic negative feedback system, LITer2.0. This circuit is composed of a self-repressing TetR protein fused with a Light-oxygen-voltage-sensing domain (LOV2), a linker sequence P2A which enables a multicistronic transcript, and GFP. When blue light is applied, the TIP sequence opens from the LOV2 domain, inhibiting the TetR protein, and allowing increased, dose-responsive transcription of both TetR and the GFP reporter. The inhibited TetR protein is unable to bind and repress at the DNA operator site, thus increasing transcription. This negative feedback keeps the gene expression noise low and allows a high-fold change of gene expression. FRT – flip recognition target, HygR – hygromycin resistance, LacZ – β-galactosidase, ZeoR – zeocin resistance, T – TetR, tetracycline repressor protein, D2ir is a CMV-based promoter with TwtO sites, LOV2 is Light-oxygen-voltage-sensing domain, TIP is tet-inhibiting peptide which inhibits the TetR protein. This figure has been modified from Guinn and Balázsi (2020)55. Please click here to view a larger version of this figure.
Figure 2: LPA experimental programming. (A) Experiments can be programmed using the tools listed on Tabor lab website47 with flexibility for steady-state, dynamic, or advanced LPA programming31. Furthermore, electronic files can be saved for future use for technical replicates or experimental induction of alternative cell lines. (B) LPA device with main components viewed from the top and side (C). (D) Images of foil-sealed plates to reside on LPA. LPA – Light Plate Apparatus, LED – light-emitting diode. Elements of this figure have been modified from Guinn (2019)11. Please click here to view a larger version of this figure.
Figure 3: Calibration of the light plate apparatus. (A) Representative image of LPA for LEDs set to 2000 g.s. based on IRIS software. (B) Image from panel (A) with background subtracted and threshold used to binarize the image to calculate pixel intensity of each LED. (C) Histogram of pixel intensities (determined by MATLAB image analysis) of 96 LEDs (4 LPA plates) set to the same IRIS software value (e.g., 2000 g.s.). The red line represents mean LED pixel intensity, and CV in the panel represents the coefficient of variation for the 96 wells. (D) Heatmap showing the LED intensities of the LPA image in panel (A) normalized to the maximum intensity LED. (E) Calibration value heatmap determined for the LPA image in panel (A) to create equal intensity production for each LED when programmed for the LPA. LPA – Light Plate Apparatus, LED – light-emitting diode, CV – coefficient of variation. Please click here to view a larger version of this figure.
Figure 4: Randomization of LPA and light toxicity effects. (A) Representative image of two programmable LPA LED configurations set from 25 to 4000 g.s, based on the IRIS software. The first configuration is more likely to allow systematic errors such as edge-effects of heat and light to cross over (e.g., the lower row of the LPA affecting the 2nd lowest row). The second image randomizes the location of intensities, therefore minimizing systematic error (bias) caused by edge effects. (B) Matrix showing randomization location for light intensities in the LPA shown in panel (A), which can be used to determine the intensity-dependent results of a given experiment. (C) Flow cytometry data for two different light intensities (1250 and 4000 g.s.) for 3 days of induction at continuous illumination. The black gate is based on uninduced cells. (D) Bar graph showing flow cytometry data such as panel (C) but for a wide range of light intensities. LPA – Light Plate Apparatus, FSC – forward scatter, SSC – side scatter, g.s. – light intensity value measured in grayscale. LITer cells11 had a dose-responsive decrease in cell survival that was statistically significant using ANOVA 1-tailed test with a p-value of 0.0022. Please click here to view a larger version of this figure.
Figure 5: Representative microscopy images of engineered optogenetic cell lines. Cells were imaged on an inverted microscope with a camera (14-bit) for acquiring phase contrast and fluorescence imaging. Cells were exposed for 5 ms for phase contrast (Panel A, representative bright-field image) and 50 ms for GFP/FITC acquisition (Panel B) at 100% light source intensity. Cells can be imaged at various time points depending on desired steady-state acquisition or dynamic response, leading to a steady state. Cells here represent a pulse duration titration at fixed intensity (1000 g.s.) ranging from no light exposure to 3 days of light exposure. The unit g.s represents a light intensity value measured in grayscale. This figure has been modified from Guinn (2019)11. Please click here to view a larger version of this figure.
Figure 6: Flow cytometry of engineered optogenetic cell lines. Cells were analyzed on a BD LSRFortessa flow cytometer, with approximately 10,000 cells collected within an SSC-FSC (side scatter-forward scatter) gate. The cells were then analyzed with the FCS Express software. Histogram or scatter plots could be generated to quantify the expression of the gene of interest (e.g., GFP). (A) Engineered cells plotted on SSC-FSC axes for gating within the black line. (B) Representative LITer cells induced with varying duration of light pulses at 1000 g.s. up to 72 h of induction, illustrating dose-response of gene expression. (C) Representative dose-response data for light intensity titration comparing the LITer gene circuit (TIP-based system) versus VVD or LightOn system40, which is a positive regulation system with no feedback. (D) Histogram corresponding to the VVD system, illustrating wider distribution (and therefore higher gene expression noise) compared to the LITer system. CV is the coefficient of variation, a metric for gene expression noise. FSC – forward scatter, SSC – side scatter, FITC – fluorescein isothiocyanate, g.s. – light intensity value measured in grayscale. This figure has been modified from Guinn (2019)11. Please click here to view a larger version of this figure.
Figure 7: Quantitative real-time PCR of engineered optogenetic cell lines. Representative data showing that multiple gene expression levels can be induced and quantified using light as a stimulus. The unit Rq represents relative quantification of expression fold-change compared to control. LITer cells11 had a dose-responsive increase in RNA expression that was statistically significant using ANOVA 1-tailed test with a p-value of 0.0352 and 0.0477 for GFP and KRAS levels, respectively. This figure has been modified from Guinn (2019)11. Please click here to view a larger version of this figure.
Figure 8: Immunofluorescence of engineered optogenetic cell lines. Representative data showing protein-level estimates based on immunofluorescence. (A) Protein levels of KRAS(G12V) and (B) protein levels of phosphorylated-ERK, which the LITer gene circuit induces according to increasing light amounts directly and indirectly, respectively. Data shown in bars are mean values, error bars are standard deviation (n = 3). A.u. – arbitrary units, g.s. – light intensity value measured in grayscale. LITer cells11 had a dose-responsive increase in protein levels that was statistically significant using ANOVA 1-tailed test with a p-value of 2.79E-04 and 0.016 for KRAS and phosphorylated-ERK levels, respectively. This figure has been modified from Guinn (2019)11. Please click here to view a larger version of this figure.
Readers of this article can gain insight into the steps vital for characterizing optogenetic gene circuits (as well as other gene expression systems), including 1) gene circuit design, construction, and validation; 2) cell engineering for introducing gene circuits into stable cell lines (e.g., Flp-FRT recombination); 3) induction of the engineered cells with a light-based platform such as the LPA; 4) initial characterization of light induction assays via fluorescence microscopy; and 5) final gene expression characterization with a variety of assays, including flow cytometry, quantitative real-time PCR (qRT-PCR), or immunofluorescence assays.
Additionally, the methods outlined above are highly modular for essentially any gene circuit architecture expressing genes-of-interest, with the main potential protocol modifications at the circuit construction step and the assays conducted. In the case of gene circuit construction, the flexibility of molecular cloning allows any gene of interest to be exchanged or co-expressed with a fluorescence marker with minor modifications to primer design or assembly protocols. Additionally, while the procedures outlined here focus mostly on a NF gene circuit design for precise (low-noise) gene expression control, other architectures such as positive regulation or positive feedback (PF) can be implemented to achieve different features such as high-fold change or high gene expression noise, respectively56,57. Using a variety of gene circuit architectures (e.g., PF, NF, etc.) can allow researchers to explore diverse biological questions such as the roles protein level magnitudes and noise play in drug resistance or metastasis58. The protocols listed here also focus on various ways of gene expression quantification, but any number of functional assays (e.g., cell motility, wound-healing, proliferation, etc.) can be added after microscopy acquisition with little to no effect on the preceding methods. This is especially relevant to single-cell studies where optogenetics can use spatiotemporal induction to study behaviors such as pulsatile expression dynamics59.
Complementing method modularity, troubleshooting is another important feature of each main protocol. For circuit construction, the main differences resides in the appropriate circuit-specific primer design, while later methods such as assembly and plasmid amplification via bacterial growth are more standardized and typically include their own extensive troubleshooting procedures. Cell engineering can be modified with drug titration curves depending on the cell line used. Additionally, if a commercial cell line is used, troubleshooting guides can be obtained directly from the cell line manufacturer. Also, unintended light effects are important to quantify on the engineered cell lines. For example, due to the phototoxic effects of 470 nm light, a light intensity curve should be created to investigate the induction of death or damage in a population. Once a variety of light values are tested, users can create an FSC-SSC gate or utilize a method such as propidium iodide staining to quantify dead/damaged cells, and therefore, serve as a tool to determine appropriate light ranges to use experimentally.
For LPA troubleshooting, LEDs can produce edge effects of light and heat crosstalk with adjacent wells. For example, high-intensity wells may cause some induction in adjacent wells if there is an improper seal or a large heat/light gradient between wells. These effects can be maximal if the LPA is programmed in a particular order (e.g., ascending/descending) of a light parameter. To combat this, the IRIS software allows users to employ a randomization matrix to randomize the well intensities and therefore reduce systematic error. For troubleshooting aspects of fluorescence microscopy, exposure time, gain settings (controlling camera sensitivity), and light source intensity are the main parameters that can be adjusted. Tuning these parameters can allow for optimal image production and ideal signal-to-noise ratios as well as avoiding oversaturation and phototoxicity.
For flow cytometry troubleshooting, photomultiplier tube voltages and cellular gating are the main aspects that can be adjusted. Tuning these parameters can allow ideal comparisons between experimental conditions and control samples, proper signal acquisition for both weak or strong fluorescence signals, and exclusion of cellular debris or unwanted cell populations. It should be noted that flow cytometry voltages are ideally kept constant across experiments to allow proper comparisons. Additionally, for optogenetic systems requiring multiple fluorescence outputs (e.g., FITC and PE), users will need to be aware of fluorescence compensation given crosstalk between fluorophores. In such scenarios, controls are crucial for determining proper fluorescence signals. Controls that will be necessary for users to perform include fluorescent beads, stained cells with the marker of interest, or cells constitutively expressing the fluorescence protein that can be used for creating a compensation matrix in the flow cytometry software of interest. Additionally, whether using one fluorescence marker or more, all users should routinely measure fluorescence signals of each marker for non-fluorescent cells, which yield autofluorescence/background signals. qRT-PCR troubleshooting includes varying cDNA amounts and probe design, which are often project-specific. Lastly, for immunofluorescence troubleshooting, antibody concentrations are the biggest variable for assay quantification, necessitating optimal concentration determination.
One troubleshooting aspect that is relevant to most of the above methods is the isolation effect of using a sealed plate with cells covered with foil. This method may impede the carbon dioxide gas exchange needed for the proper growth of various cell lines. From previous experimental experience, this was not a significant impediment to cellular growth for a 3-5 day experiment using the engineered cell lines within this manuscript but may become important for other cell lines or experimental durations. To address this concern, one adaptation implemented with the sealed plates includes using carbon dioxide independent media, which has not affected the growth of cells used in this study. It should be noted for other assays or cell lines where metabolism, pH levels, and cell densities are important, other interventions may be needed such as changing media or nutrients more frequently.
The limits of the methods outlined here reside in the gene circuit used, the optogenetic technology precision, and LPA interface with other equipment such as fluorescence microscopes. The technology described here is affordable, quick to build, and easy to validate46 for populations of optogenetically engineered cells. However, there are certain spatial limitations, such as the inability to control single cells without modifications to the light induction setup, such as utilizing digital mirror devices (DMDs), with recently demonstrated benefits in living cells60,61. Furthermore, the methods outlined here are limited in live-cell tracking during optogenetic stimulation, allowing only for the end-point data acquisition of the entire experimental time course.
Despite these limits, the optogenetic methods outlined here are complementary to the existing technology such as the DMDs, which have the flexibility to control single cells in real-time but are limited by the number of samples one can stimulate. Furthermore, the stable cell line engineering methods discussed here contrast existing synthetic biology methods which often characterize engineered genetic systems via transient transfections. Transient transfection approaches are inherently noisier due to the greater gene circuit copy number variation among cells. In contrast, the methods presented here involve monoclonal cell variants, each containing one gene circuit copy. Stable cell lines allow exploring cellular aspects such as gene expression variation, protein level effects on phenotypic landscapes, and single-cell methods with finer precision since there is higher confidence that each cell is identical to its neighbors.
The work here demonstrates a platform for designing any genomically integrated optogenetic gene circuit of interest, inducing such systems with reliable experimental tools, and characterizing them with a variety of gene expression and functional/phenotypic assays in a standardized manner. Future improvements of these protocols may include the integration of these methods with live-cell tracking using other optogenetic technologies such as the DMDs, which will allow spatiotemporal control of gene expression and functional applications at the single-cell level. Such advances will allow light utilization to produce the gene expression pattern of interest in single cells, establish transcription/translation dynamics, quantify protein levels, and study their role in diverse biological processes, including cell migration, proliferation, metastasis, and differentiation.
The authors have nothing to disclose.
We would like to thank Balázsi lab for comments and suggestions, Dr. Karl P. Gerhardt and Dr. Jeffrey J. Tabor for helping us construct the first LPA, and Dr. Wilfried Weber for sharing the LOV2-degron plasmids. This work was supported by the National Institutes of Health [R35 GM122561 and T32 GM008444]; The Laufer Center for Physical and Quantitative Biology; and a National Defense Science and Engineering Graduate (NDSEG) Fellowship. Funding for open access charge: NIH [R35 GM122561].
Author contributions: M.T.G. and G.B. conceived the project. M.T.G., D.C., and L.G., performed the experiments. M.T.G., D.C., L.G., and G.B. analyzed the data and prepared the manuscript. G.B. and M.T.G. supervised the project.
0.2 mL PCR tubes | Eppendorf | 951010006 | reagent for carrying out PCR |
0.25% Trypsin EDTA 1X | Thermo Fisher Scientific | MT25053CI | reagent for splitting & harvesting mammalian cells |
0.5-10 μL Adjustable Volume Pipette | Eppendorf | 3123000020 | tool used for pipetting reactions |
100-1000 μL Adjustable Volume Pipette | Eppendorf | 3123000039 | tool used for pipetting reactions |
20-200 μL Adjustable Volume Pipette | Eppendorf | 3123000055 | tool used for pipetting reactions |
2-20 μL Adjustable Volume Pipette | Eppendorf | 3123000039 | tool used for pipetting reactions |
5 mL Polystyene Round-Bottom Tube w/ Cell Strainer Cap | Corning | 352235 | reagent for flow cytometry |
5702R Centrifuge, with 4 x 100 Rotor, 15 and 50 mL Adapters, 120 V | Eppendorf | 22628113 | equipment for mammalian culture work |
Agarose | Denville Scientific | GR140-500 | reagent for gel electrophoresis |
Aluminum Foils for 96-well Plates | VWR® | 60941-126 | tool used for covering plates in light-induction experiments |
Ampicillin | Sigma Aldrich | A9518-5G | reagent for selecting bacteria with correct plasmid |
Analog vortex mixer | Thermo Fisher Scientific | 02215365PR | tool for carrying PCR, transformation, or gel extraction reactions |
Bacto Dehydrated Agar | Fisher Scientific | DF0140010 | reagent for growing bacteria |
BD LSRAria | BD | 656700 | tool for sorting engineered cell lines into monoclonal populations |
BD LSRFortessa | BD | 649225 | tool for characterizing engineered cell lines |
BSA, Bovine Serum Albumine | Government Scientific Source | SIGA4919-1G | reagent for IF incubation buffer |
Cell Culture Plate 12-well, Clear, flat-bottom w/lid, polystyrene, non-pyrogenic, standard-TC | Corning | 353043 | plate used for growing monoclonal cells |
Centrifuge | VWR | 22628113 | instrument for mammalian cell culture |
Chemical fume hood | N/A | N/A | instrument for carrying out IF reactions |
Clear Cell Culture Plate 24 well flat-bottom w/ lid | BD | 353047 | plate used for growing monoclonal cells |
CytoOne T25 filter cap TC flask | USA Scientific | CC7682-4825 | container for growing mammalian cells |
Dimethyl sulfoxide (DMSO) | Fischer Scientific | BP231-100 | reagent used for freezing down engineered mammalian cells |
Ethidium Bromide | Thermo Fisher Scientific | 15-585-011 | reagent for gel electrophoresis |
Falcon 96 Well Clear Flat Bottom TC-Treated Culture Microplate, with Lid | Corning | 353072 | container for growing sorted monoclonal cells |
FCS Express | De Novo Software: | N/A | software for characterizing flow cytometry data |
Fetal Bovine Serum, Regular, USDA 500 mL | Corning | 35-010-CV | reagent for growing mammalian cells |
Fisherbrand Petri Dishes with Clear Lid – Raised ridge; 100 x 15 mm | Fisher Scientific | FB0875712 | equipment for growing bacteria |
Gibco DMEM, High Glucose | Thermo Fisher Scientific | 11-965-092 | reagent for growing mammalian cells |
Hs00932330_m1 KRAS isoform a Taqman Gene Expression Assay | Life Technologies | 4331182 | qPCR Probe |
Hygromycin B (50 mg/mL), 20 mL | Life Technologies | 10687-010 | reagent for selecting cells with proper gene circuit integration |
iScript Reverse Transcription Supermix | Bio-Rad Laboratories | 1708890 | reagent for converting RNA to cDNA |
Laboratory Freezer -20 °C | VWR | 76210-392 | equipment for storing experimental reagents |
Laboratory Freezer -80 °C | Panasonic | MDF-U74VC | equipment for storing experimental reagents |
Laboratory Refrigerator +4 °C | VWR | 76359-220 | equipment for storing experimental reagents |
LB Broth (Lennox) , 1 kg | Sigma-Aldrich | L3022-250G | reagent for growing bacteria |
LIPOFECTAMINE 3000 | Life Technologies | L3000008 | reagemt for transfecting gene circuits into mammalian cells |
MATLAB 2019 | MathWorks | N/A | software for analyzing experimental data |
Methanol | Acros Organics | 413775000 | reagent for immunofluorescence reaction |
Microcentrifuge Tubes, Polypropylene 1.7 mL | VWR | 20170-333 | plasticware container |
Mr04097229_mr EGFP/YFP Taqman Gene Expression Assay | Life Technologies | 4331182 | qPCR Probe |
MultiTherm Shaker | Benchmark Scientific | H5000-HC | equipment for bacterial transformation |
NanoDrop Lite Spectrophotometer | Thermo Fisher Scientific | ND-NDL-US-CAN | equipment for DNA/RNA concentration measurement |
NEB Q5 High-Fidelity DNA polymerase 2x Master Mix | NEB | M0492S | reagent for PCR of gene circuit fragments |
NEB10-beta Competent E. coli (High Efficiency) | New England Biolabs (NEB) | C3019H | bacterial cells for amplifying gene circuit of interest |
NEBuilder HiFi DNA Assembly Master Mix | New England Biolabs (NEB) | E2621L | reagent for combining gene circuit fragements |
Nikon Eclipse Ti-E inverted microscope with a DS-Qi2 camera | Nikon Instruments Inc. | N/A | instrument for quantifying gene expression |
NIS-Elements | Nikon Instruments Inc. | N/A | software for characterizing fluorescence microscopy data |
oligonucleotides | IDT | N/A | reagent used for PCR of gene circuit components |
Panasonic MCO-170 AICUVHL-PA cellIQ Series CO2 Incubator with UV and H2O2 Control | Panasonic | MCO-170AICUVHL-PA | instrument for growing mammalian cells |
Paraformaldehyde, 16% Electron Microscopy Grade | Electron Microscopy Sciences | 15710-S | reagent |
PBS, Dulbecco's Phosphate-Buffered Saline (D-PBS) (1x) | Invitrogen | 14190144 | reagent for mammalian cell culture,reagent for IF incubation buffer |
Penicillin-Streptomycin (10,000 U/mL), 100x | Fisher Scientific | 15140-122 | reagent for growing mammalian cells |
primary ERK antibody | Cell Signaling Technology | 4370S | primary ERK antibody for immunifluorescence |
primary KRAS antibody | Sigma-Aldrich | WH0003845M1 | primary KRAS antibody for immunifluorescence |
QIAprep Spin Miniprep Kit (250) | Qiagen | 27106 | reagent kit for purifying gene circuit plasmids |
QIAquick Gel Extraction Kit (50) | Qiagen | 28704 | reagent kit for purifying gene circuit fragments |
QuantStudio 3 Real-Time PCR System | Eppendorf | A28137 | equipment for qRT-PCR |
Relative Quantification App | Thermo Fisher Scientific | N/A | software for quantifying RNA/cDNA amplificaiton |
RNeasy Plus Mini Kit | Qiagen | 74134 | kit for extracting RNA of engineered mammalian cells |
Secondary ERK antibody | Cell Signaling Technology | 8889S | secondary ERK antibody for immunifluorescence |
secondary KRAS antibody | Invitrogen | A11005 | secondary KRAS antibody for immunifluorescence |
Serological Pipets 5.0 mL | Olympus Plastics | 12-102 | reagents used for setting up a variety of chemical reactions |
SmartView Pro Imager System | Major Science | UVCI-1200 | tool for imaging correct PCR bands |
SnapGene Viewer (free) or SnapGene | SnapGene | N/A | software DNA sequence design and analysis |
Stage top incubator | Tokai Hit | INU-TIZ | tool for carrying PCR, transformation, or gel extraction reactions |
TaqMan Fast Advanced Master Mix | Thermo Fisher Scientific | 4444557 | reagent for PCR of gene circuit fragments |
TaqMan Human GAPD (GAPDH) Endogenous Control (VIC/MGB probe), primer limited, 2500 rxn | Life Technologies | 4326317E | qPCR Probe |
Thermocycler | Bio-Rad | 1851148 | tool for carrying PCR, transformation, or gel extraction reactions |
VisiPlate-24 Black, Black 24-well Microplate with Clear Bottom, Sterile and Tissue Culture Treated | PerkinElmer | 1450-605 | plate used for light-induction experiments |
VWR Disposable Pasteur Pipets, Glass, Borosilicate Glass Pipet, Short Tip, Capacity=2 mL, Overall Length=14.6 cm | VWR | 14673-010 | reagent for mammalian cell culture |
VWR Mini Horizontal Electrophoresis Systems, Mini10 Gel System | VWR | 89032-290 | equipment for DNA gel electrophoresis |
Flp-In 293 | Thermo Fisher Scientific | R75007 | Engineered cell line with FRT site |