JoVE Logo

Sign In

A subscription to JoVE is required to view this content. Sign in or start your free trial.

In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Results
  • Discussion
  • Disclosures
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

Complex genetic circuits are time-consuming to design, test, and optimize. To facilitate this process, mammalian cells are transfected in a way that allows the testing of multiple stoichiometries of circuit components in a single well. This protocol outlines the steps for experimental planning, transfection, and data analysis.

Abstract

Mammalian genetic circuits have demonstrated the potential to sense and treat a wide range of disease states, but optimization of the levels of circuit components remains challenging and labor-intensive. To accelerate this process, our lab developed poly-transfection, a high-throughput extension of traditional mammalian transfection. In poly-transfection, each cell in the transfected population essentially performs a different experiment, testing the behavior of the circuit at different DNA copy numbers and allowing users to analyze a large number of stoichiometries in a single-pot reaction. So far, poly-transfections that optimize ratios of three-component circuits in a single well of cells have been demonstrated; in principle, the same method can be used for the development of even larger circuits. Poly-transfection results can be easily applied to find optimal ratios of DNA to co-transfect for transient circuits or to choose expression levels for circuit components for the generation of stable cell lines.

Here, we demonstrate the use of poly-transfection to optimize a three-component circuit. The protocol begins with experimental design principles and explains how poly-transfection builds upon traditional co-transfection methods. Next, poly-transfection of cells is carried out and followed by flow cytometry a few days later. Finally, the data is analyzed by examining slices of the single-cell flow cytometry data that correspond to subsets of cells with certain component ratios. In the lab, poly-transfection has been used to optimize cell classifiers, feedback and feedforward controllers, bistable motifs, and many more. This simple but powerful method speeds up design cycles for complex genetic circuits in mammalian cells.

Introduction

The field of mammalian synthetic biology has rapidly progressed, from developing simple sense-and-respond parts in cultured cell lines to the optimization of complex networks of genes to address real-world challenges in diagnostics and therapeutics1. These sophisticated circuits are capable of sensing biological inputs from microRNA profiles to cytokines to small molecule drugs, and implementing logic processing circuits including transistors, band-pass filters, toggle switches, and oscillators. They have also shown promising results in animal models of diseases like cancer, arthritis, diabetes, and many more1,2,3,4,5. However, as the complexity of a circuit grows, optimizing the levels of each of its components becomes increasingly challenging.

One particularly useful type of genetic circuit is a cell classifier, which can be programmed to sense and respond to cellular states. Selective production of protein or RNA outputs in specific cellular states is a powerful tool to guide and program differentiation of cells and organoids, identify and destroy diseased cells and/or undesirable cell types, and regulate the function of therapeutic cells1,2,3,4,5. However, creating circuits in mammalian cells that can accurately classify cell states from multiple cellular RNA and/or protein species has been highly challenging.

One of the most time-consuming steps of developing a cell classification circuit is to optimize the relative expression levels of individual component genes, such as sensors and processing factors, within the circuit. To speed up circuit optimization and allow for the construction of more sophisticated circuits, recent work has used mathematical modeling of cell classifier circuits and their components to predict optimal compositions and topologies6,7. While this has shown powerful results so far, mathematical analysis is limited by the need to systematically characterize the input-output behavior of component genes in the circuit, which is time-consuming. Further, a myriad of context-dependent problems can emerge in complex genetic circuits, causing the behavior of a full circuit to defy predictions based on individual part characterizations8,9.

To more rapidly develop and test complex mammalian circuits such as cell state classifiers, our lab developed a technique called poly-transfection10, an evolution of plasmid co-transfection protocols. In co-transfection, multiple plasmid DNA species are complexed together with a positively charged lipid or polymer reagent, then delivered to cells in a correlated manner (Figure 1A). In poly-transfection, plasmids are separately complexed with the reagent, such that the DNA from each transfection complex is delivered to cells in a de-correlated manner (Figure 1B). Using this method, cells within the transfected population are exposed to numerous combinations of ratios of two or more DNA payloads carrying different circuit components.

To measure the ratios of circuit components delivered to each cell, each transfection complex within a poly-transfection contains a constitutively expressed fluorescent reporter that serves as a proxy for cellular uptake of the complex. Filler DNA that does not contain any elements active within a mammalian cell is used to tune the relative amount of the fluorescent reporter and circuit components delivered to a cell in a single transfection complex and is discussed in more detail in the discussion. An example of filler DNA used in the Weiss lab is a plasmid containing a terminator sequence, but no promotor, coding sequence, etc. Cells with different ratios of circuit components can then be compared to find optimal ratios for gene circuit function. This in turn yields useful predictions for choosing promoters and other circuit elements to achieve optimal gene expression levels when combining circuit components into a single vector for genetic integration (e.g., a lentivirus, transposon, or landing pad). Thus, instead of choosing ratios between circuit components based on intuition or via a time-consuming trial and error process, poly-transfection evaluates a wide range of stoichiometries between genetic parts in a single-pot reaction.

In our lab, poly-transfection has enabled the optimization of many genetic circuits, including cell classifiers, feedback and feedforward controllers, and bistable motifs. This simple but powerful method significantly speeds up design cycles for complex genetic circuits in mammalian cells. Poly-transfection has since been used to characterize several genetic circuits to reveal their multi-dimensional input-output transfer functions at high resolution10, optimize an alternate circuit topology for cell state classification11, and accelerate various published12,13 and ongoing projects.

Here we describe and depict the workflow for using poly-transfection to rapidly optimize a genetic circuit (Figure 2). The protocol shows how to generate high-quality poly-transfection data and avoid several common errors in the poly-transfection protocol and data analysis (Figure 3). It then demonstrates how to use poly-transfection to characterize simple circuit components and, in the process, benchmark poly-transfection results against co-transfection (Figure 4). Finally, the results of poly-transfection show optimization of the cancer classifier circuit (Figure 5).

Protocol

NOTE: Table 1 and Table 2 serve as significant references for this protocol. Table 1 shows reagent scaling for reactions, and Table 2 shows DNA ratio arithmetic for an example poly-transfection described in the protocol (upper half) and for a possible follow-up experiment (lower half).

1. Preparing cells for transfection

  1. Ensure that the culture of human embryonic kidney (HEK293) cells is 60%-80% confluent before initiating the protocol. To do this, seed 1 x 106 cells in a 100 mm x 15 mm tissue culture Petri dish 2 days prior, and incubate at 37 °C with 5% CO2.
    NOTE: Although our protocol focuses on HEK293 cells, other cell types may be substituted.
  2. Prepare the media and cells for transfections as described below.
    1. Pre-warm at least 20 mL of a solution of Dulbecco's modified eagle medium (DMEM) with 10% fetal bovine serum (FBS) and 1% non-essential amino acids (NEAA; see Table of Materials) to 37 °C. Pre-warm at least 2.4 mL of Trypsin and 2.4 mL of phosphate-buffered saline (PBS) to 37 °C as well. Pre-warm reduced serum medium to ~16 °C.
      NOTE: All tissue culture work should be performed with care in a biosafety cabinet.
  3. Resuspend the cells in the DMEM solution as described below.
    1. Aspirate and dispose of the current media. Dispense 2 mL of PBS onto the HEK293 cell culture to wash the cells. Aspirate and dispose of the PBS.
    2. Dispense 2 mL of Trypsin onto the HEK293 cell culture. Place the Petri dish in an incubator at 37 °C for 3 min or until the cells no longer adhere to the dish. Return the dish to the biosafety cabinet and dilute the cell solution by dispensing 8 mL of DMEM solution into the plate.
    3. Mix the solution by gently pipetting up and down several times. Aspirate all the media and place it in a 15 mL conical tube.
  4. Centrifuge the cells at 300 x g for 3 min to pellet them. Aspirate the media (taking care not to aspirate the cells) and discard it. Resuspend the cells in 5 mL of DMEM solution, mixing by gently pipetting up and down.
  5. Estimate the current cell concentration using an automated cell counter (listed in the Table of Materials). Seed six wells (in this example) in a 24-well plate with 1 x 105 cells (for a seeding density of 50,000 cells/cm2).
  6. Add the DMEM solution up to 500 µL (add DMEM solution to the wells first), and then label one well per treatment as the following: no color control, mKO2 control, TagBFP control, NeonGreen control, all color control, and poly-transfection 1. TagBFP, mKO2, and NeonGreen control wells are single color controls for all fluorescent proteins included in the poly-transfection.

2. Performing transfection

  1. Prepare tubes for each DNA aggregate. Set aside 1.5 mL microcentrifuge tubes and label the tubes as: no color control, mKO2 control, TagBFP control, NeonGreen control, all color control, poly-transfection mix 1, and poly-transfection mix 2.
    1. Add 36 µL of reduced serum medium to the no color control, mKO2 control, TagBFP control, NeonGreen control, and all color control tubes. Add 18 µL of reduced serum medium to each of the poly-transfection mix 1 and poly-transfection mix 2 tubes.
      NOTE: The plasmid concentrations are assumed to be 150 ng/µL.
    2. Add 600 ng of filler plasmid to the no color control tube. Add 300 ng of mKO2 and 300 ng of filler plasmid to the mKO2 color control tube. Add 300 ng of TagBFP and 300 ng of filler plasmid to the TagBFP color control tube.
    3. Add 300 ng of constitutive NeonGreen plasmid and 300 ng of filler plasmid to the NeonGreen color control tube. Add 100 ng each of mKO2, TagBFP, and constitutive NeonGreen, as well as 300 ng of filler plasmid, to the all color control tube.
    4. Add 150 ng of mKO2 to the poly-transfection mix 1 tube. Add 75 ng of reporter NeonGreen plasmid and 75 ng of filler plasmid to the poly-transfection mix 1 tube.
    5. Add 150 ng of TagBFP to the poly-transfection mix 2 tube. Add 75 ng of L7ae plasmid and 75 ng of filler plasmid to the poly-transfection mix 2 tube.
  2. Create the transfection master mix in a 1.5 mL microcentrifuge tube by combining 216 µL of reduced serum medium with 9.48 µL of transfection reagent (see Table 1 for reagent ratios and reaction scaling). Mix well by pipetting up and down, and set aside.
  3. Add 1.58 µL of enhancer reagent to each of the no color control, single color control, and all color control tubes. Add 79 µL of enhancer reagent to each of the poly-transfection mix tubes. Mix each tube individually by pipetting vigorously.
  4. Add the transfection master mix to each tube containing DNA.
    1. Add 37.58 µL of transfection master mix to each of the no color control, single color control, and all color control tubes. Mix each tube individually by pipetting vigorously.
    2. Add 18.79 µL of transfection master mix to each of the poly-transfection mix tubes. Mix each tube individually by pipetting vigorously.
  5. Dispense the transfection mixes into the wells.
    1. Pipette 65.97 µL of each transfection mix for the no color, single color, and all color controls into the corresponding wells.
    2. Pipette 32.98 µL of the poly-transfection mix 1 into the poly-transfection well and swirl the plate quickly but gently in a tight figure-eight pattern along a flat surface to distribute the complexes effectively. Then, pipette 32.98 µL of the poly-transfection mix 2 into the same poly-transfection well and swirl the plate in the same fashion.
  6. Place the plate in an incubator at 37 °C, with 5% CO2 and without shaking, for a period of 48 h.
    NOTE: To increase cell viability, the cell media can be replaced every 6 h following transfection (though this is not always necessary, and with HEK293 cells and its derivatives, one must be careful not to detach the cells from the plate when changing the media).
ReagentAmountScaling
Reduced serum medium for DNA mixture36 μL per control tube, 18 μL per poly-transfection tube 0.05 μL Reduced serum medium/ng DNA per tube, with 10-20% extra volume to account for pipetting
DNA300-600 ng per tube
P30001.58 μL per control tube, 0.79 μL per poly-transfection tube0.0022 μL P3000/ng DNA per tube, with 10-20% extra
Reduced serum medium for Lipo master mix36 μL per control tube, 18 μL per poly-transfection tube0.05 μL reduced serum medium/ng total DNA, with 10-20% extra volume to account for pipetting
Transfection and enhancer reagent1.58 μL per control tube, 0.79 μL per poly-transfection tube0.0022 μL Lipofectamine 3000/ng DNA, with 10-20% extra

Table 1: Reagent scaling for transfections. The table indicates the correct ratio of reagent to include for the DNA quantity included in a single well. This can be used to scale reactions effectively and form master mixes. Quantities of reagent have been scaled to include a 20% excess.

3. Preparing cells for flow cytometry

  1. Pre-warm at least 4.2 mL of the DMEM solution to 37 °C. Pre-warm at least 4.2 mL of Trypsin and 4.2 mL of PBS to 37 °C. Keep fluorescence-activated cell sorting (FACS) buffer solution at 4 °C until ready to use.
  2. Resuspend the cells in the FACS buffer solution (PBS supplemented with 1% BSA, 5 mM ethylenediaminetetraacetic acid [EDTA], and 0.1% sodium azide [NaN3], to reduce clumping; see Table of Materials) as described below.
    1. Aspirate and dispose of the current media in each well. Dispense 5 mL of PBS into each well to wash the cells. Aspirate and dispose of the PBS.
    2. Dispense 5 mL of Trypsin into each well. Place the plate in an incubator at 37 °C for 3 min, or until the cells no longer adhere to the dish. Return the plate to the biosafety cabinet and dilute the cell solutions by dispensing 5 mL of DMEM solution into each well.
    3. For each well, mix the solution by gently pipetting up and down several times. For each well, aspirate all the media and place it in a 15 mL conical tube.
  3. Centrifuge the cells at 300 x g for 3 min to pellet them. Aspirate the media, taking care not to aspirate the cells and discard it. In each tube, resuspend the cells in 5 mL of FACS buffer solution, mixing by gently pipetting up and down.
  4. Pass each cell suspension solution through a strainer (to remove clumps) into separate flow cytometry conical tubes. Keep these tubes on ice for no more than 1 h, and perform flow cytometry as soon as possible.

4. Performing flow cytometry

NOTE: Operating a flow cytometer requires proper training and knowledge of the necessary tasks. As software and equipment may vary and users should be trained generally, this section refers to specific operations that are useful to perform.

  1. First, examine the cells transfected with the filler plasmid control (no color control) to select for cell characteristics and avoid anomalies (including aggregates, debris, etc.). While there are many combinations of parameters to distinguish cells, use the following three general options as a good way to visualize distinguishing features.
    1. Look at the side scatter area (log or linear scale per preference/cell type) versus the forward scatter area (linear scale).
    2. Look at the side scatter height (log scale) versus the side scatter width (linear scale).
    3. Look at the forward scatter width (linear scale) versus the forward scatter height (linear scale).
  2. Next, look at the single color controls. Use the all color control to tune the instrument voltages, such that the signals from each fluorescent protein are normalized to equivalent arbitrary units (a.u.) of fluorescence. Next, run the single color controls for each fluorescent protein, which are used to set the compensation matrix, allowing for bleed-through correction.
    NOTE: Ideally, the full dynamic range of the fluorescence values should be visible. Further normalization of fluorescent protein signals can be done via conversion to standardized units (e.g., molecules of equivalent fluorescein [MEFLs; see Beal et al.15]). To enable MEFL conversion during analysis, run rainbow calibration beads. Such calibration is also useful for reducing instrument-to-instrument and day-to-day signal variation16.
  3. Run the poly-transfection sample tube.
    NOTE: Where possible, it is recommended to run 1,000 x 10^ (^ = mixes) cells, as higher-dimensional poly-transfections need to be subdivided into more bins during analysis, and each bin needs enough cells (ideally >10) to make statistically significant comparisons.

5. Performing post-experiment analysis

  1. Initially, use data from the controls (and, if applicable, the beads) to ensure accurate results. Use one of the available software tools to perform live cell gating (using gates outlined above), compensation, and autofluorescence correction.
    NOTE: We typically use either custom MATLAB code (e.g., https://github.com/jonesr18/MATLAB_Flow_Analysis or Cytoflow17), which has both a graphical user interface and a python library suitable for the pre-processing phase and for poly-transfection analysis.
MethodComplexng Fluorescent Markerng L7ae (fraction)ng Reporter (fraction)ng Filler DNA (fraction)Total (ng)
Poly-transfection 1600
115075 (½)75 (½)300
215075 (½)75 (½)300
Poly-transfection 2600
115025 (1/6)125 (5/6)300
2150125 (5/6)25(1/6)300

Table 2: DNA amounts for poly-transfection demonstrated in the protocol, and an example follow-up experiment with tuned plasmid ratios. The upper half of the table shows the composition of plasmids used in a simple poly-transfection experiment. The lower half shows the composition of an updated experiment that adjusts the plasmid ratios to better subsample a hypothetical concentration space, where the gene expression modulator is at a more optimal 1:5 ratio relative to its reporter, yielding more transfected cells to sample around this ratio.

Results

In Figure 1, we compare co-transfection to poly-transfection. In a co-transfection, all plasmids are delivered in the same transfection mix, resulting in high correlation between the amount of each plasmid any single cell receives (Figure 1A). While the number of total plasmids delivered to each cell varies significantly, the fluorescence of the two reporter proteins in the individual cells across the population is well-correlated, indicating that the two plasmi...

Discussion

Rapid prototyping methods such as computer-aided design (CAD), breadboarding, and 3D printing have revolutionized mechanical, electrical, and civil engineering disciplines. The ability to quickly search through many possible solutions to a given challenge greatly accelerates progress in a field. We believe that poly-transfection is an analogous technology for biological engineering, enabling rapid prototyping of genetic circuits. Additionally, other rapid prototyping technologies require hands-on sequential iteration of ...

Disclosures

R.W. is a co-founder of Strand Therapeutics and Replay Bio; R.W. and R.J. filed a provisional patent related to a cell type classifier.

Acknowledgements

We would like to thank former Weiss Lab members that led or contributed to developing the poly-transfection method and its application to cell classifiers: Jeremy Gam, Bre DiAndreth, and Jin Huh; other Weiss lab members who have contributed to further method development/optimization: Wenlong Xu, Lei Wang, and Christian Cuba-Samaniego; Prof. Josh Leonard and group members, including Patrick Donahue and Hailey Edelstein, for testing poly-transfection and providing feedback; and Prof. Nika Shakiba for inviting this manuscript and providing feedback. We would also like to thank the National Institutes of Health [R01CA173712, R01CA207029, P50GM098792]; National Science Foundation [1745645]; Cancer Center Support (core) Grant [P30CCA14051, in part] from the NCI, and National Institutes of Health [P50GM098792] for funding this work.

Materials

NameCompanyCatalog NumberComments
15mL Corning Falcon conical tubesThermoFisher Scientific14-959-53A
24-well petri dishAny company of choice(Non-pyrogenic, Sterile, RNase, DNase, DNA and Pyrogen Free)
Bovine serum albuminNEBB9000S
CentrifugeAny company of choiceCapable of exposing 15mL Falcon tubes to 300 rcf
Countess 3 Automated Cell CounterThermoFisher ScientificAMQAX2000
Countess Cell Counting Chamber SlidesThermoFisher ScientificC10228
CytoflowNon-commercial software packagehttps://cytoflow.readthedocs.io/en/stable/# 
DMEMVWR10-013-CVUse the correct media for your cell type
EDTA ThermoFisher Scientific03690-100ML
Fetal bovine serumSigma AldrichF4135
HEK cellsATCCCRL-1573Use the relevant cell type for your experiments. HEK cells tend to transfect very efficiently.
HeLa cellsATCCCRL-12401Use the relevant cell type for your experiments.
Lipofectamine 3000 and P3000 enhancerThermoFisher ScientificL3000001Use the correct reagent for your cell type; transfection and enhancer reagent
LSRFortessa flow cytometerBD BiosciencesN/A
MEM Non-Essential Amino Acids SolutionGibco11140050
Microcentrifuge Tubes, 1.5 mLAny company of choice
Opti-MEMThermoFisher Scientific31985070reduced serum medium
Phosphate buffered salineThermoFisher Scientific70011044
Rainbow calibration beadsSpherotechURCP-100-2H
Sodium azideSigma AldrichS2002
TrypsinVWR25-053-CI

References

  1. Prochazka, L., Benenson, Y., Zandstra, P. W. Synthetic gene circuits and cellular decision-making in human pluripotent stem cells. Current Opinion in Systems Biology. 5, 93-103 (2017).
  2. Sayeg, M. K., et al. Rationally designed microRNA-based genetic classifiers target specific neurons in the brain. ACS Synthetic Biology. 4 (7), 788-795 (2015).
  3. Zhen, X., Wroblewska, L., Prochazka, L., Weiss, R., Benenson, Y. Multi-Input RNAi-based logic circuit for identification of specific cancer cells. Science. 333 (6047), 1307-1311 (2011).
  4. Nissim, L., et al. Synthetic RNA-based immunomodulatory gene circuits for cancer immunotherapy. Cell. 171 (5), 1138-1150 (2017).
  5. Nissim, L., Bar-Ziv, R. H. A tunable dual-promoter integrator for targeting of cancer cells. Molecular Systems Biology. 6, 444 (2010).
  6. Mohammadi, P., Castel, S. E., Brown, A. A., Lappalainen, T. Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change. Genome Research. 27 (11), 1872-1884 (2017).
  7. Prochazka, L., et al. Discrete-to-analog signal pluripotent stem cells conversion in human. BioRxiv. , (2021).
  8. Del Vecchio, D. Modularity, context-dependence, and insulation in engineered biological circuits. Trends in Biotechnology. 33 (2), 111-119 (2015).
  9. Shakiba, N., Jones, R. D., Weiss, R., Del Vecchio, D. Context-aware synthetic biology by controller design: Engineering the mammalian cell. Cell Systems. 12 (6), 561-592 (2021).
  10. Gam, J. J., DiAndreth, B., Jones, R. D., Huh, J., Weiss, R. A 'poly-transfection' method for rapid, one-pot characterization and optimization of genetic systems. Nucleic Acids Research. 47 (18), 106 (2019).
  11. Jones, R. D., et al. Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles. Nature Communications. 13 (1), 1720 (2022).
  12. Jones, R. D., et al. An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells. Nature Communications. 11 (1), 5690 (2020).
  13. DiAndreth, B., Wauford, N., Hu, E., Palacios, S., Weiss, R. PERSIST platform provides programmable RNA regulation using CRISPR endoRNases. Nature Communications. 13 (1), 2582 (2022).
  14. Frei, T., et al. Characterization and mitigation of gene expression burden in mammalian cells. Nature Communications. 11 (1), 4641 (2020).
  15. Beal, J., Weiss, R., Yaman, F., Adler, A., Davidsohn, N. A method for fast, high-precision characterization of synthetic biology devices. Computer Science and Artificial Intelligence Laboratory Technical Report. Massachusetts institute of technology. , (2012).
  16. Beal, J., et al. Meeting measurement precision requirements for effective engineering of genetic regulatory networks. ACS Synthetic Biology. 11 (3), 1196-1207 (2022).
  17. Teague, B. Cytoflow: A Python toolbox for flow cytometry. bioRxiv. , (2022).
  18. Ferreira, J. P., Overton, K. W., Wang, C. L. Tuning gene expression with synthetic upstream open reading frames). Proceedings of the National Academy of Sciences. 110 (28), 11284-11289 (2013).
  19. Michaels, Y. S., et al. Precise tuning of gene expression levels in mammalian cells. Nature Communications. 10 (1), 818 (2019).
  20. Saito, H., et al. Synthetic translational regulation by an L7Ae-kink-turn RNP switch. Nature Chemical Biology. 6 (1), 71-78 (2010).
  21. Wroblewska, L., et al. Mammalian synthetic circuits with RNA binding proteins for RNA-only delivery. Nature Biotechnology. 33 (8), 839-841 (2015).
  22. Wagner, T. E., et al. Small-molecule-based regulation of RNA-delivered circuits in mammalian cells. Nature Chemical Biology. 14 (11), 1043-1050 (2018).
  23. Sekuklu, S. D., Donoghue, M. T. A., Spillane, C. miR-21 as a key regulator of oncogenic processes. Biochemical Society Transactions. 37, 918-925 (2009).
  24. Stanton, B. C., et al. Genomic mining of prokaryotic repressors for orthogonal logic gates. Nature Chemical Biology. 10 (2), 99-105 (2014).
  25. Stanton, B. C., et al. Systematic transfer of prokaryotic sensors and circuits to mammalian cells. ACS Synthetic Biology. 3 (12), 880-891 (2014).

Reprints and Permissions

Request permission to reuse the text or figures of this JoVE article

Request Permission

Explore More Articles

Cell State IdentificationPoly transfectionSynthetic BiologyCircuit ComponentsFlow CytometryExperimental EfficiencyDNA AggregatesReduced Serum MediumFiller PlasmidTransfection ReagentEnhancer Reagent

This article has been published

Video Coming Soon

JoVE Logo

Privacy

Terms of Use

Policies

Research

Education

ABOUT JoVE

Copyright © 2025 MyJoVE Corporation. All rights reserved