Name: rapid development of cell state identification circuits with poly-transfection
Uploaded: 2023-02-24T14:40:00
Description: Watch this Scientific Journal Video about Rapid Development of Cell State Identification Circuits with Poly-Transfection at JoVE.com

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

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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 (Video) | JoVE 

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.

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 ...

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