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.

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
<ol>
	<li>Ensure that the culture of human embryonic kidney (HEK293) cells is 60%-80% co...

<ol>
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K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rationally+designed+microRNA-based+genetic+classifiers+target+specific+neurons+in+the+brain.">Rationally designed microRNA-based genetic classifiers target specific neurons in the brain.</a> ACS Synthetic Biology. 4 (7), 788-795 (2015).</li><li>Zhen, X., Wroblewska, L., Prochazka, L., Weiss, R., Benenson, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-Input+RNAi-based+logic+circuit+for+identification+of+specific+cancer+cells.">Multi-Input RNAi-based logic circuit for identification of specific cancer cells.</a> Science. 333 (6047), 1307-1311 (2011).</li><li>Nissim, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+RNA-based+immunomodulatory+gene+circuits+for+cancer+immunotherapy.">Synthetic RNA-based immunomodulatory gene circuits for cancer immunotherapy.</a> Cell. 171 (5), 1138-1150 (2017).</li><li>Nissim, L., Bar-Ziv, R. 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D., Weiss, R., Del Vecchio, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Context-aware+synthetic+biology+by+controller+design:+Engineering+the+mammalian+cell.">Context-aware synthetic biology by controller design: Engineering the mammalian cell.</a> Cell Systems. 12 (6), 561-592 (2021).</li><li>Gam, J. J., DiAndreth, B., Jones, R. D., Huh, J., Weiss, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+'poly-transfection'+method+for+rapid,+one-pot+characterization+and+optimization+of+genetic+systems.">A 'poly-transfection' method for rapid, one-pot characterization and optimization of genetic systems.</a> Nucleic Acids Research. 47 (18), 106 (2019).</li><li>Jones, R. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+and+tunable+signal+processing+in+mammalian+cells+via+engineered+covalent+modification+cycles.">Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles.</a> Nature Communications. 13 (1), 1720 (2022).</li><li>Jones, R. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+endoribonuclease-based+feedforward+controller+for+decoupling+resource-limited+genetic+modules+in+mammalian+cells.">An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells.</a> Nature Communications. 11 (1), 5690 (2020).</li><li>DiAndreth, B., Wauford, N., Hu, E., Palacios, S., Weiss, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PERSIST+platform+provides+programmable+RNA+regulation+using+CRISPR+endoRNases.">PERSIST platform provides programmable RNA regulation using CRISPR endoRNases.</a> Nature Communications. 13 (1), 2582 (2022).</li><li>Frei, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+and+mitigation+of+gene+expression+burden+in+mammalian+cells.">Characterization and mitigation of gene expression burden in mammalian cells.</a> Nature Communications. 11 (1), 4641 (2020).</li><li>Beal, J., Weiss, R., Yaman, F., Adler, A., Davidsohn, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+fast,+high-precision+characterization+of+synthetic+biology+devices.+Computer+Science+and+Artificial+Intelligence+Laboratory+Technical+Report.">A method for fast, high-precision characterization of synthetic biology devices. Computer Science and Artificial Intelligence Laboratory Technical Report.</a> Massachusetts institute of technology. , (2012).</li><li>Beal, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Meeting+measurement+precision+requirements+for+effective+engineering+of+genetic+regulatory+networks.">Meeting measurement precision requirements for effective engineering of genetic regulatory networks.</a> ACS Synthetic Biology. 11 (3), 1196-1207 (2022).</li><li>Teague, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytoflow:+A+Python+toolbox+for+flow+cytometry.">Cytoflow: A Python toolbox for flow cytometry.</a> bioRxiv. , (2022).</li><li>Ferreira, J. P., Overton, K. W., Wang, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tuning+gene+expression+with+synthetic+upstream+open+reading+frames).">Tuning gene expression with synthetic upstream open reading frames).</a> Proceedings of the National Academy of Sciences. 110 (28), 11284-11289 (2013).</li><li>Michaels, Y. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Precise+tuning+of+gene+expression+levels+in+mammalian+cells.">Precise tuning of gene expression levels in mammalian cells.</a> Nature Communications. 10 (1), 818 (2019).</li><li>Saito, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+translational+regulation+by+an+L7Ae-kink-turn+RNP+switch.">Synthetic translational regulation by an L7Ae-kink-turn RNP switch.</a> Nature Chemical Biology. 6 (1), 71-78 (2010).</li><li>Wroblewska, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mammalian+synthetic+circuits+with+RNA+binding+proteins+for+RNA-only+delivery.">Mammalian synthetic circuits with RNA binding proteins for RNA-only delivery.</a> Nature Biotechnology. 33 (8), 839-841 (2015).</li><li>Wagner, T. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small-molecule-based+regulation+of+RNA-delivered+circuits+in+mammalian+cells.">Small-molecule-based regulation of RNA-delivered circuits in mammalian cells.</a> Nature Chemical Biology. 14 (11), 1043-1050 (2018).</li><li>Sekuklu, S. D., Donoghue, M. T. A., Spillane, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miR-21+as+a+key+regulator+of+oncogenic+processes.">miR-21 as a key regulator of oncogenic processes.</a> Biochemical Society Transactions. 37, 918-925 (2009).</li><li>Stanton, B. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+mining+of+prokaryotic+repressors+for+orthogonal+logic+gates.">Genomic mining of prokaryotic repressors for orthogonal logic gates.</a> Nature Chemical Biology. 10 (2), 99-105 (2014).</li><li>Stanton, B. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+transfer+of+prokaryotic+sensors+and+circuits+to+mammalian+cells.">Systematic transfer of prokaryotic sensors and circuits to mammalian cells.</a> ACS Synthetic Biology. 3 (12), 880-891 (2014).</li></ol>

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 ...

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).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>15mL Corning Falcon conical tubes</td>
			<td>ThermoFisher Scientific</td>
			<td>14-959-53A</td>
			<td></td>
		</tr>
		<tr>
			<td>24-well petri dish</td>
			<td>Any company of choice</td>
			<td></td>
			<td>(Non-pyrogenic, Sterile, RNase, DNase, DNA and Pyrogen Free)</td>
		</tr>
		<tr>
			<td>Bovine serum albumin</td>
			<td>NEB</td>
			<td>B9000S</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Any company of choice</td>
			<td></td>
			<td>Capable of exposing 15mL Falcon tubes to 300 rcf</td>
		</tr>
		<tr>
			<td>Countess 3 Automated Cell Counter</td>
			<td>ThermoFisher Scientific</td>
			<td>AMQAX2000</td>
			<td></td>
		</tr>
		<tr>
			<td>Countess Cell Counting Chamber Slides</td>
			<td>ThermoFisher Scientific</td>
			<td>C10228</td>
			<td></td>
		</tr>
		<tr>
			<td>Cytoflow</td>
			<td>Non-commercial software package</td>
			<td></td>
			<td>https://cytoflow.readthedocs.io/en/stable/#&#160;</td>
		</tr>
		<tr>
			<td>DMEM</td>
			<td>VWR</td>
			<td>10-013-CV</td>
			<td>Use the correct media for your cell type</td>
		</tr>
		<tr>
			<td>EDTA&#160;</td>
			<td>ThermoFisher Scientific</td>
			<td>03690-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal bovine serum</td>
			<td>Sigma Aldrich</td>
			<td>F4135</td>
			<td></td>
		</tr>
		<tr>
			<td>HEK cells</td>
			<td>ATCC</td>
			<td>CRL-1573</td>
			<td>Use the relevant cell type for your experiments. HEK cells tend to transfect very efficiently.</td>
		</tr>
		<tr>
			<td>HeLa cells</td>
			<td>ATCC</td>
			<td>CRL-12401</td>
			<td>Use the relevant cell type for your experiments.</td>
		</tr>
		<tr>
			<td>Lipofectamine 3000 and P3000 enhancer</td>
			<td>ThermoFisher Scientific</td>
			<td>L3000001</td>
			<td>Use the correct reagent for your cell type; transfection and enhancer reagent</td>
		</tr>
		<tr>
			<td>LSRFortessa flow cytometer</td>
			<td>BD Biosciences</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>MEM Non-Essential Amino Acids Solution</td>
			<td>Gibco</td>
			<td>11140050</td>
			<td></td>
		</tr>
		<tr>
			<td>Microcentrifuge Tubes, 1.5 mL</td>
			<td>Any company of choice</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Opti-MEM</td>
			<td>ThermoFisher Scientific</td>
			<td>31985070</td>
			<td>reduced serum medium</td>
		</tr>
		<tr>
			<td>Phosphate buffered saline</td>
			<td>ThermoFisher Scientific</td>
			<td>70011044</td>
			<td></td>
		</tr>
		<tr>
			<td>Rainbow calibration beads</td>
			<td>Spherotech</td>
			<td>URCP-100-2H</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium azide</td>
			<td>Sigma Aldrich</td>
			<td>S2002</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin</td>
			<td>VWR</td>
			<td>25-053-CI</td>
			<td></td>
		</tr>
	</tbody>
</table>

rapid development of cell state identification circuits with poly-transfection

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&#160;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.

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...

Watch this Scientific Journal Video about Rapid Development of Cell State Identification Circuits with Poly-Transfection at JoVE.com

Rapid Development of Cell State Identification Circuits with Poly-Transfection

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.

Mammalian genetic circuits have demonstrated the potential to sense and treat a wide range of disease states, but optimization of the levels of circuit ...

Our protocol makes it easier and faster to understand relationships between biological parts. We can test the behavior of many ratio metric combinations of parts in a single well. This technique allows researchers to characterize the complex relationships between circuit components more comprehensively with greater experimental efficiency than conventional approaches.This method is applicable for synthetic biology and for any biological questions, probing behavioral changes in a cell in response to different levels of transfected genes where the output can be measured via flow cytometry. Begin preparing tubes for each DNA aggregate by setting aside 1.5-milliliter micro centrifuge tubes labeled as no color control, mKO2 control, TagBFP control, neon green control, all color control, poly-transfection mix one and poly-transfection mix two. Add 36 microliters of reduced serum medium to the no color control, mKO2 control, TagBFP control, neon green control and all color control tubes, then add 18 microliters of reduced serum medium to each poly-transfection mix one and poly-transfection mix two tubes.Next, add 600 nanograms of filler plasmid to the no color control tube, 300 nanograms of mKO2 and 300 nanograms of filler plasmid to the mKO2 color control tube and add 300 nanograms of TagBFP and 300 nanograms of filler plasmid to the TagBFP color control tube. To the neon green color control tube, add 300 nanograms of constitutive neon green and 300 nanograms of filler plasmid, then add 100 nanograms of each mKO2, TagBFP, constitutive neon green and 300 nanograms of filler plasmid to the all color control tube. To the poly-transfection mix one tube, add 150 nanograms of mKO2, 75 nanograms of reporter neon green plasmid and 75 nanograms of filler plasmid.To the poly-transfection mix two tube, add 150 nanograms of TagBFP, 75 nanograms of L7Ae plasmid and 75 nanograms of filler plasmid. Once done, create the transfection master mix in a 1.5-milliliter micro centrifuge tube by combining 216 microliters of reduced serum medium with 9.48 microliters of transfection reagent. Mix well by pipetting up and down before setting it aside.Add 1.58 microliters of enhancer reagent to no color control, single color control and all color control tubes and add 79 microliters of enhancer reagent to each poly-transfection mix tubes. Mix each tube individually by pipetting vigorously. Add 37.58 microliters of transfection master mix to the no color control, single color control and all color control tubes and mix by pipetting vigorously.Next, add 18.79 microliters of transfection master mix to each poly-transfection mix tubes and mix each tube well with vigorous pipetting. Dispense the transfection mixes into the wells by pipetting 65.97 microliters of each transfection mix for the no color, single color and all color controls into the corresponding wells, then transfer 32.98 microliters of the poly-transfection mix one into the poly-transfection well and distribute the complexes effectively by swirling the plate quickly but gently in a tight, figurate pattern along a flat surface, then pipette 32.98 microliters of the poly-transfection mix two into the same poly-transfection well and swirl the plate in the same fashion. Incubate cells for two days.Perform flow cytometry to read out the fluorescence of the transfection markers and output reporter for each cell. A well-performed co-transfection, distinct from the poly-transfection shown in the video, shows a tight correlation between TagBFP and EYFP, expressing plasmids that were co-delivered. In contrast, a poorly performed co-transfection shows a poor correlation between the two plasmids.Well-performed poly-transfection showed good coverage of the two-dimensional space and good compensation of any spectral bleed through between fluorescent proteins. Poly-transfection data with a low number of live cells was difficult to subdivide into enough bins with a sufficient number of cells in each bin for analysis. A poly-transfection with poor transfection efficiency resulted in sparse coverage of the two-dimensional space for analysis.The effective DNA ratio on output expression was tested by placing a repressor protein, L7Ae with mKO2, and an output protein, neon green with TagBFP, in separate transfection mixes. Neon green expression was then monitored at different levels of mKO2 and TagBFP. Co-transfection and poly-transfection results were compared for this circuit.Results of 11 individual co-transfections that combined the DNA parts shown previously were collated into a single dataset and compared to the poly-transfection data. Comparison of the dose response curves of L7Ae repressing the output reporter showed that the median output level per bin was very similar between the co-transfection and poly-transfection data. A successful application of poly-transfection was demonstrated for optimizing a cell type classifier.The micro RNA-21 present in HeLa cells and not-HEK cells acts to knock down the expression of BM3R1 because BM3R1 represses production of the circuit output mKO2. When micro RNA 21 is present in a cell, mKO2 expression will be turned on. The amount of Gal4-VP16 tunes mKO2 expression at low levels of BM3R1.Each circuit component was co-delivered with a different fluorescent marker. This poly-transfection quantifies how much of each circuit component each cell received. mKO2 is produced in MIR21, producing HeLa cells but not in HEK cells.mKO2 output was analyzed after delivering these circuit components in separate transfection mixes with distinct fluorescent reporters to indicate the amount of each circuit component received by a particular cell. This sub-sampling predicted that at this ratio, the co-transfected circuit had a 91%specificity, 62%sensitivity and 77%accuracy, when classifying HEK293 versus HeLa cells. The most common pain point in this protocol is a lack of a tight correlation within each transfection mix.So remember to vigorously mix the transfection tubes right after adding the enhancer reagent and then be gentle with mixing and pipetting the transfection tubes after the lipid containing transfection mix is added The genetic components in this procedure could be replaced with any other genetic components to answer questions about their functioning performance in various environments. This method has rapidly developed genetic circuits, such as cell classifiers, feedback and feedforward controllers, bi-stable motifs and many more.

rapid-development-cell-state-identification-circuits-with-poly

Research

JoVE Journal

Bioengineering

1.2K 次观看.  Massachusetts Institute of Technology. 我们的协议可以更容易、更快速地理解生物部件之间的关系。我们可以在单个孔中测试许多比率度量组合部件的行为。该技术使研究人员能够以比传统方法更高的实验效率更全面地表征电路组件之间的复杂关系。该方法适用于合成生物学和任何生物学问题，探测细胞中的行为变化，以响应不同水平的转染基因，其中可以通过流式细胞术测量输出。通过搁置标记为无颜色?...