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% 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 &#176;C with 5% CO2. 
	NOTE:&#160;Although our protocol focuses on HEK293 cells, other cell types may be substituted.</li>
	<li>Prepare the media and cells for transfections as described below.
	<ol>
		<li>Pre-warm at least 20 mL of a solution of Dulbecco&#39;s modified eagle medium (DMEM) with 10% fetal bovine serum (FBS) and 1% non-essential amino acids (NEAA; see Table of Materials) to 37 &#176;C. Pre-warm at least 2.4 mL of Trypsin and 2.4 mL of phosphate-buffered saline (PBS) to 37 &#176;C as well. Pre-warm reduced serum medium to ~16 &#176;C. 
		NOTE: All tissue culture work should be performed with care in a biosafety cabinet.</li>
	</ol>
	</li>
	<li>Resuspend the cells in the DMEM solution as described below.
	<ol>
		<li>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.</li>
		<li>Dispense 2 mL of Trypsin onto the HEK293 cell culture. Place the Petri dish in an incubator at 37 &#176;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.</li>
		<li>Mix the solution by gently pipetting up and down several times. Aspirate all the media and place it in a 15 mL conical tube.</li>
	</ol>
	</li>
	<li>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.</li>
	<li>Estimate&#160;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).</li>
	<li>Add the DMEM solution up to 500 &#181;L (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.&#160;TagBFP, mKO2, and NeonGreen control wells are single color controls for all fluorescent proteins included in the poly-transfection.</li>
</ol>
2. Performing transfection
<ol>
	<li>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.
	<ol>
		<li>Add 36 &#181;L of reduced serum medium to the no color control, mKO2 control, TagBFP control, NeonGreen control, and all color control tubes. Add 18 &#181;L 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/&#181;L.</li>
		<li>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.</li>
		<li>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.</li>
		<li>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.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Create the transfection master mix in a 1.5 mL microcentrifuge tube by combining 216 &#181;L of reduced serum medium with 9.48 &#181;L of transfection reagent (see Table 1 for reagent ratios and reaction scaling). Mix well by pipetting up and down, and set aside.</li>
	<li>Add 1.58 &#181;L of enhancer reagent to each of the no color control, single color control, and all color control tubes. Add 79 &#181;L of enhancer reagent to each of the poly-transfection mix tubes. Mix each tube individually by pipetting vigorously.</li>
	<li>Add the transfection master mix to each tube containing DNA.
	<ol>
		<li>Add 37.58 &#181;L 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.</li>
		<li>Add 18.79 &#181;L of transfection master mix to each of the poly-transfection mix tubes. Mix each tube individually by pipetting vigorously.</li>
	</ol>
	</li>
	<li>Dispense the transfection mixes into the wells.
	<ol>
		<li>Pipette 65.97 &#181;L of each transfection mix for the no color, single color, and all color controls into the corresponding wells.</li>
		<li>Pipette 32.98 &#181;L 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 &#181;L of the poly-transfection mix 2 into the same poly-transfection well and swirl the plate in the same fashion.</li>
	</ol>
	</li>
	<li>Place the plate in an incubator at 37 &#176;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).</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagent</td>
			<td colspan="3">Amount</td>
			<td colspan="3">Scaling</td>
		</tr>
		<tr>
			<td>Reduced serum medium for DNA mixture</td>
			<td colspan="3">36 &#956;L per control tube, 18 &#956;L per poly-transfection tube&#160;</td>
			<td colspan="3">0.05 &#956;L Reduced serum medium/ng DNA per tube, with 10-20% extra volume to account for pipetting</td>
		</tr>
		<tr>
			<td>DNA</td>
			<td colspan="3">300-600 ng per tube</td>
			<td colspan="3"></td>
		</tr>
		<tr>
			<td>P3000</td>
			<td colspan="3">1.58 &#956;L per control tube, 0.79 &#956;L per poly-transfection tube</td>
			<td colspan="3">0.0022 &#956;L P3000/ng DNA per tube, with 10-20% extra</td>
		</tr>
		<tr>
			<td>Reduced serum medium for Lipo master mix</td>
			<td colspan="3">36 &#956;L per control tube, 18 &#956;L per poly-transfection tube</td>
			<td colspan="3">0.05 &#956;L reduced serum medium/ng total DNA, with 10-20% extra volume to account for pipetting</td>
		</tr>
		<tr>
			<td>Transfection and enhancer reagent</td>
			<td colspan="3">1.58 &#956;L per control tube, 0.79 &#956;L per poly-transfection tube</td>
			<td colspan="3">0.0022 &#956;L Lipofectamine 3000/ng DNA, with 10-20% extra</td>
		</tr>
	</tbody>
</table>
Table 1:&#160;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
<ol>
	<li>Pre-warm at least 4.2 mL of the DMEM solution to 37 &#176;C. Pre-warm at least 4.2 mL of Trypsin and 4.2 mL of PBS to 37 &#176;C. Keep fluorescence-activated cell sorting (FACS) buffer solution at 4 &#176;C until ready to use.</li>
	<li>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.
	<ol>
		<li>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.</li>
		<li>Dispense 5 mL of Trypsin into each well. Place the plate in an incubator at 37 &#176;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.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>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.</li>
	<li>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.</li>
</ol>
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.
<ol>
	<li>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.
	<ol>
		<li>Look at the side scatter area (log or linear scale per preference/cell type) versus the forward scatter area (linear scale).</li>
		<li>Look at the side scatter height (log scale) versus the side scatter width (linear scale).</li>
		<li>Look at the forward scatter width (linear scale) versus the forward scatter height (linear scale).</li>
	</ol>
	</li>
	<li>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.</li>
	<li>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 &#62;10) to make statistically significant comparisons.</li>
</ol>
5. Performing post-experiment analysis
<ol>
	<li>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.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Method</td>
			<td>Complex</td>
			<td>ng Fluorescent Marker</td>
			<td>ng L7ae (fraction)</td>
			<td>ng Reporter (fraction)</td>
			<td>ng Filler DNA (fraction)</td>
			<td>Total (ng)</td>
		</tr>
		<tr>
			<td>Poly-transfection 1</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>600</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>1</td>
			<td>150</td>
			<td>75 (&#189;)</td>
			<td></td>
			<td>75 (&#189;)</td>
			<td>300</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>2</td>
			<td>150</td>
			<td></td>
			<td>75 (&#189;)</td>
			<td>75 (&#189;)</td>
			<td>300</td>
		</tr>
		<tr>
			<td>Poly-transfection 2</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>600</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>1</td>
			<td>150</td>
			<td>25 (1/6)</td>
			<td></td>
			<td>125 (5/6)</td>
			<td>300</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>2</td>
			<td>150</td>
			<td></td>
			<td>125 (5/6)</td>
			<td>25(1/6)</td>
			<td>300</td>
		</tr>
	</tbody>
</table>
Table 2:&#160;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.

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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.

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 multiple possible solutions, whereas poly-transfection is able to explore many solutions simultaneously. Poly-transfection enables a large number of combinations of genetic circuit component ratios to be tested in a single well and has been used to optimize several published genetic circuits10,11,13. The experimental protocol is a simple extension of standard co-transfection, allowing for straightforward adoption by many mammalian cell researchers. Generally, a very close agreement between poly-transfection and co-transfection results is seen10 (an example is Figure 4). Thus, poly-transfection can be used in most cases where plasmid ratios are titrated within co-transfection mixes and outputs are measured at the single-cell level.
The complexity and scale of poly-transfection increase with the complexity of genetic circuits to optimize. As the number of dimensions to analyze increases, the combinatorial ratios of different parts and the number of cells needed for their analysis increases exponentially. For example, to optimize the classifier in Figure 5, cells were transfected in a 6-well plate format, and data was collected on at least 1.5 million live cells. We have also optimized a classifier with an additional circuit component10, necessitating transfection at a 10 cm plate scale and the collection of millions of cells. At that scale and above, DNA production is time-consuming, and transfection reagents are expensive. That said, poly-transfection uses orders of magnitude less cells, DNA, and transfection reagents than testing a comparable number of circuit component ratio combinations in individual wells. Additionally, a considerable amount of time is saved by making orders of magnitude fewer transfection mixes.
Poly-transfection enables advanced analysis methods for flow cytometry data. The two main approaches are (1) binning cells according to the expression of each transfection marker and (2) extracting cells at defined ratios of transfection markers, thereby simulating co-transfection. The former selects cells with a specific combination of DNA dosage for each complex (and thus each part), yielding input-output transfer functions. The latter selects cells at a specific ratio of DNA dosages for each part, simulating co-transfection at such a ratio of plasmid DNA masses. Plotting the expression level of circuit output reporter(s) in the subsampled selections gives a measure of output at the defined levels or ratios of inputs. For circuit optimization, one can identify the best performing bins/ratios as defined by the purpose of the circuit-in the case of cell classifiers, these are the bins/ratios where the circuit is ON in the target cell type and OFF in non-target cell types. When choosing a bin size, one should take into consideration the level of precision required, as well as the number of cells per bin. Smaller bins focus on the ideal combination of circuit components more precisely, but if a bin has too few cells, it can introduce undesirable noise to the measurements.
Future improvements in computational analysis of poly-transfection data may facilitate optimization even with low cell coverage per bin/ratio. Currently, we exclude data from bins with fewer than a set threshold of cells (e.g., 10) to avoid overly noisy measurements. In combination with repeated measurements, this enables more accurate and precise calculations of dose-response curves, classifier accuracies, and other metrics defined on a per-bin basis. However, each cell in poly-transfection can be considered to be an independent experiment, measuring at a fine resolution the circuit output(s) at a precise level of inputs. With this in mind, mechanistic and phenotypic models of dose responses and circuit optimizations can directly fit to the distribution of gene expression from each marker and reporter, rather than their binned summary statistics10. This enables more robust measurements and takes advantage of the many individual data points collected per experiment. Such modeling approaches could thereby yield predictive circuit characterization and optimization even with sparsely sampled high-dimensional poly-transfections. Further, machine learning methods such as response surface methodology and random forest regression may be used to analyze relatively sparse, high-dimensional data10.
An alternate approach to increasingly large poly-transfections is to sequentially optimize circuits using hierarchies of poly-transfections. In this approach, first, poly-transfection is used to optimize a module comprising a subset of genetic components within a larger circuit. Then, these optimized modules are delivered as separate poly-transfection mixes, enabling one to find the optimal ratio/dosage of each circuit module. This approach uses a significantly lower number of cells, DNA, and transfection reagents. However, groups of components are not necessarily modular with respect to other components, and thus an optimal ratio of component parts in a module measured in isolation could be suboptimal within the larger circuit context8.
Poly-transfection methods are also limited by the laser/filter configuration of the flow cytometer used to collect data. In addition to measured fluorescent outputs, each transfection mix contains a fluorescent protein marker. Thus, it is critical to select fluorescent proteins that can be well compensated on the cytometer. We previously systematically analyzed the bleed-through of a panel of 22 fluorescent proteins on a five-laser flow cytometer (the BD LSRFortessa), and found that the set of Sirius, TagBFP, mNeonGreen, mKO2, and iRFP720 could be used together and compensated well without significant issues10. However, the optimization of larger circuits may require advanced cytometry methods, such as spectral cytometry to separate fluorescent proteins with more overlapping spectra, which our lab is&#160;currently optimizing&#160;with up to eight fluorescent proteins. Databases such as the fluorescent protein database (https://www.fpbase.org) are useful for selecting fluorescent proteins with particular spectral overlap. However, this process can be complicated by various factors, some of which are specific to particular cytometers. For instance, the usage of certain red proteins like tdTomato may result in undesirable bleed-through into blue channels only in certain laser/filter configurations10. Additionally, several far-red proteins have shown non-linear relationships between DNA dosage and fluorescence output10, reducing their use as effective markers for DNA dosage.
For consistency across experiments that are performed with varying numbers of complexes (one, two, three, etc.), it is useful to use the same fractional amount of plasmid per transfection mix. For example, if testing a three-gene system with each gene encoded in one of three plasmids (A, B, and C), one could co-transfect the circuit plasmids at a 1:1:1 ratio along with an equal ratio of a plasmid encoding a transfection marker. This would make each plasmid one-quarter of the total mass of the mix, and thus approximately one-quarter of the mass of each transfection complex that is formed. When poly-transfecting A, B, and C in separate complexes with their own reporters, instead of mixing the plasmids 1:1 with reporters or maintaining their total DNA mass, it is more consistent to keep each plasmid at one-quarter of the mass of each complex, with filler DNA taking up the remaining mass (see Table 5 for examples). This is because the distribution of gene expression among transfected cells depends less on the total mass of DNA delivered in the complexes, and more on the fractional amount of DNA in each complex10. If ratios other than 1:1:1 are desired, calculate the corresponding fractions of the total DNA mass in the transfection mix for each part. For example, Table 5 [bottom] shows a 1:1:4 ratio.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Method</td>
			<td>Complex</td>
			<td>ng A (fraction)</td>
			<td>ng B (fraction)</td>
			<td>ng C (fraction)</td>
			<td>ng Reporter (fraction)</td>
			<td>ng Filler DNA (fraction)</td>
			<td>Total (ng)</td>
		</tr>
		<tr>
			<td>Co-transfection</td>
			<td>1</td>
			<td>150 (&#188;)</td>
			<td>150 (&#188;)</td>
			<td>150 (&#188;)</td>
			<td>150 (&#188;)</td>
			<td>0 (0)</td>
			<td>600</td>
		</tr>
		<tr>
			<td>Poly-transfection</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>600</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>1</td>
			<td>50 (&#188;)</td>
			<td></td>
			<td></td>
			<td>50 (&#188;)</td>
			<td>100 (&#189;)</td>
			<td>200</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>2</td>
			<td></td>
			<td>50 (&#188;)</td>
			<td></td>
			<td>50 (&#188;)</td>
			<td>100 (&#189;)</td>
			<td>200</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>3</td>
			<td></td>
			<td></td>
			<td>50 (&#188;)</td>
			<td>50 (&#188;)</td>
			<td>100 (&#189;)</td>
			<td>200</td>
		</tr>
		<tr>
			<td>Co-transfection</td>
			<td>1</td>
			<td>75 (&#8539;)</td>
			<td>75 (&#8539;)</td>
			<td>300 (&#189;)</td>
			<td>150 (&#188;)</td>
			<td>0 (0)</td>
			<td>600</td>
		</tr>
		<tr>
			<td>Poly-transfection</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>600</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>1</td>
			<td>25 (&#8539;)</td>
			<td></td>
			<td></td>
			<td>50 (&#188;)</td>
			<td>125 (&#8541;)</td>
			<td>200</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>2</td>
			<td></td>
			<td>25 (&#8539;)</td>
			<td></td>
			<td>50 (&#188;)</td>
			<td>125 (&#8541;)</td>
			<td>200</td>
		</tr>
		<tr>
			<td>&#8594;</td>
			<td>3</td>
			<td></td>
			<td></td>
			<td>100 (&#189;)</td>
			<td>50 (&#188;)</td>
			<td>50 (&#188;)</td>
			<td>200</td>
		</tr>
	</tbody>
</table>
Table 5:&#160;Examples of using filler DNA for consistency across co- and poly-transfection experiments. Here, two generic examples of co-transfecting three plasmids with the same amount of filler DNA is shown. In the top example, the plasmids are all at equal masses. In the bottom example, one plasmid is delivered at a higher mass compared to the others. The fraction of total DNA that would be used in a co-transfection complex is transmuted to poly-transfection complexes, with the remaining amount of DNA (up to total amount for each complex, which is fixed and equal across complexes) constituted with filler DNA.
The overall objective of filler DNA is to maintain similar dosages of plasmids per cell, regardless of the number of unique transfection mixes. As a crude approximation of this principle, separating three plasmids into three mixes while maintaining the same DNA mass of each (and the appropriate ratio of transfection reagent) reduces the percent of cells receiving a given complex by one third compared to a single mix containing all three plasmids. However, each transfected cell subsequently receives a ~3x higher gene dosage. Reducing the relative amount of the plasmids alone without filler DNA is thus insufficient to maintain consistent plasmid dosages, as this simply decreases transfection efficiency without altering the underlying distribution of expression among the transfected cells10. On the other hand, filler DNA allows for adjusting the DNA dosage without affecting the overall transfection efficiency (though detectable transfected cells may decrease due to lower signal)10. Following the crude approximation above, reducing the fraction of DNA in the poly-transfection mixes to one-third and adding filler DNA maintains the relative gene dosages compared to the original co-transfection. Filler DNA therefore ensures efficient and accurate transfection complex formation.
To alter the range of expression of a gene of interest covered by its reporter, the fraction of each test plasmid and/or promoters used to drive the gene there can be tuned relative to the reporter. If a part is strong/highly active at low DNA dosages, using a lower DNA fraction can help center the dynamic range of the part in the middle of the fluorescence distribution of the reporter in transfected cells. Likewise, for parts that are weak/only active at high DNA dosages, using a higher fraction can be useful. However, care must be taken with such tuning, as reducing DNA fractions too much increases the stochasticity of delivery to the cells compared to the reporter10, and high gene expression levels can overload cellular gene expression machinery12,14. To avoid stochasticity, and in cases where each gene is delivered to cells at a fixed ratio (e.g., if the circuit is to be generated as a single lenti, PiggyBac, or Landing Pad vector), the relative expression of each gene can be tuned using stronger/weaker promoters, small uORFs18, and/or miSFITs19.
Though the protocol describes a reverse transfection technique, poly-transfection is also possible with forward transfection. In reverse transfection, the cells are simultaneously seeded and transfected; in forward transfection, the cells are transfected ~24 h after first plating at approximately one-half the density that would be used in reverse transfection (to allow for cell division by the time of transfection). In general, reverse transfection is more efficient but also more toxic, and we have noticed some differences in the shape of transfection distributions based on the reagent, transfection time, and cell line. Thus, the transfection method of choice should be optimized for each cell line to maximize the number of transfected cells and coverage of the multi-dimensional concentration space of plasmid dosages per cell.
Overall, poly-transfection enables the rapid optimization of mammalian genetic circuits. Many possible ratiometric combinations of circuit components can be easily tested in a single well. Additionally, since poly-transfection contains more information about the levels of each part in a system than a conventional transfection, it has been found to be highly valuable for characterizing the behavior of various genetic parts10,11,12,13. The adoption of poly-transfection is anticipated to accelerate the pace of developing new and improved gene circuits for use in mammalian cells.

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><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/#&amp;nbsp;</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&amp;nbsp;</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.

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 ...

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

Nanyang Technological University

Research

JoVE Journal

Bioengineering

1.3K Views.  Massachusetts Institute of Technology. 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. 

Video: Rapid Development of Cell State Identification Circuits with Poly-Transfection

Improved Generation of Induced Cardiomyocytes Using a Polycistronic Construct Expressing Optimal Ratio of Gata4, Mef2c and Tbx5

Direct conversion of cardiac fibroblasts (CFs) into induced cardiomyocytes (iCMs) holds great potential for regenerative medicine by offering alternative strategies for treatment of heart disease. This conversion has been achieved by forced expression of defined factors such as Gata4 (G), Mef2c (M) and Tbx5 (T). Traditionally, iCMs are generated by a cocktail of viruses expressing these individual factors. However, reprogramming efficiency is relatively low and most of the in vitro G,M,T-transduced fibroblasts do not become fully reprogrammed, making it difficult to study the reprogramming mechanisms. We recently have shown that the stoichiometry of G,M,T is crucial for efficient iCM reprogramming. An optimal stoichiometry of G,M,T with relative high level of M and low levels of G and T achieved by using our polycistronic MGT vector (hereafter referred to as MGT) significantly increased reprogramming efficiency and improved iCM quality in vitro. Here we provide a detailed description of the methodology used to generate iCMs with MGT construct from cardiac fibroblasts. Isolation of cardiac fibroblasts, generation of virus for reprogramming and evaluation of the reprogramming process are also included to provide a platform for efficient and reproducible generation of iCMs.

We describe here a protocol for the generation of iCMs using retrovirus-mediated delivery of Gata4, Tbx5 and Mef2c in a polycistronic construct. This protocol yields a relatively homogeneous population of reprogrammed cells with improved efficiency and quality and is valuable for future studies of iCM reprogramming.

Direct conversion of cardiac fibroblasts (CFs) into induced cardiomyocytes (iCMs) holds great potential for regenerative medicine by offering alternative ...

Developmental Biology

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

The fast-growing biopharmaceutical industry demands speedy development of highly efficient and reliable production systems to meet the increasing requirement for drug supplies. The generation of production cell lines has traditionally involved manual operations that are labor-intensive, low-throughput and vulnerable to human errors. We report here an integrated high-throughput and automated platform for development of manufacturing cell lines for the production of protein therapeutics.
The combination of BD FACS Aria Cell Sorter, CloneSelect Imager and TECAN Freedom EVO liquid handling system has enabled a high-throughput and more efficient cell line development process. In this operation, production host cells are first transfected with an expression vector carrying the gene of interest 1, followed by the treatment with a selection agent. The stably-transfected cells are then stained with fluorescence-labeled anti-human IgG antibody, and are subsequently subject to flow cytometry analysis 2-4. Highly productive cells are selected based on fluorescence intensity and are isolated by single-cell sorting on a BD FACSAria. Colony formation from single-cell stage was detected microscopically and a series of time-laps digital images are taken by CloneSelect Imager for the documentation of cell line history. After single clones have formed, these clones were screened for productivity by ELISA performed on a TECAN Freedom EVO liquid handling system. Approximately 2,000 - 10,000 clones can be screened per operation cycle with the current system setup.
This integrated approach has been used to generate high producing Chinese hamster ovary (CHO) cell lines for the production of therapeutic monoclonal antibody (mAb) as well as their fusion proteins. With the aid of different types of detecting probes, the method can be used for developing other protein therapeutics or be applied to other production host systems. Comparing to the traditional manual procedure, this automated platform demonstrated advantages of significantly increased capacity, ensured clonality, traceability in cell line history with electronic documentation and much reduced opportunity in operator error.

A high-throughput, automated platform of manufacturing cell line development for producing protein therapeutics is described. Implementation of BD FACS Aria Cell Sorter, CloneSelect Imager and TECAN Freedom EVO liquid handling system has demonstrated significantly increased processing capacity in cell line development with improved cell line quality and high reproducibility.

The fast-growing biopharmaceutical industry demands speedy development of highly efficient and reliable production systems to meet the increasing ...

Medicine

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling pathway is one of such essential molecular machinery for cell-to-cell communications, which plays key roles in normal development of embryos. This pathway involves a cell-to-cell transfer of oscillatory information with ultradian rhythms, but despite the progress in molecular biology techniques, it has been challenging to elucidate the impact of multicellular interactions on oscillatory gene dynamics. Here, we present a protocol that permits optogenetic control and live monitoring of gene expression patterns in a precise temporal manner. This method successfully revealed that intracellular and intercellular periodic inputs of Notch signaling entrain intrinsic oscillations by frequency tuning and phase shifting at the single-cell resolution. This approach is applicable to the analysis of the dynamic features of various signaling pathways, providing a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Here, we present a protocol to analyze cell-to-cell transfer of oscillatory information by optogenetic control and live monitoring of gene expression. This approach provides a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling ...

Genetics

Real-time Live Imaging of T-cell Signaling Complex Formation

Protection against infectious diseases is mediated by the immune system 1,2. T lymphocytes are the master coordinators of the immune system, regulating the activation and responses of multiple immune cells 3,4. T-cell activation is dependent on the recognition of specific antigens displayed by antigen presenting cells (APCs). The T-cell antigen receptor (TCR) is specific to each T-cell clone and determines antigen specificity 5. The binding of the TCR to the antigen induces the phosphorylation of components of the TCR complex. In order to promote T-cell activation, this signal must be transduced from the membrane to the cytoplasm and the nucleus, initiating various crucial responses such as recruitment of signaling proteins to the TCR;APC site (the immune synapse), their molecular activation, cytoskeletal rearrangement, elevation of intracellular calcium concentration, and changes in gene expression 6,7. The correct initiation and termination of activating signals is crucial for appropriate T-cell responses. The activity of signaling proteins is dependent on the formation and termination of protein-protein interactions, post translational modifications such as protein phosphorylation, formation of protein complexes, protein ubiquitylation and the recruitment of proteins to various cellular sites 8. Understanding the inner workings of the T-cell activation process is crucial for both immunological research and clinical applications.
Various assays have been developed in order to investigate protein-protein interactions; however, biochemical assays, such as the widely used co-immunoprecipitation method, do not allow protein location to be discerned, thus precluding the observation of valuable insights into the dynamics of cellular mechanisms. Additionally, these bulk assays usually combine proteins from many different cells that might be at different stages of the investigated cellular process. This can have a detrimental effect on temporal resolution. The use of real-time imaging of live cells allows both the spatial tracking of proteins and the ability to temporally distinguish between signaling events, thus shedding light on the dynamics of the process 9,10. We present a method of real-time imaging of signaling-complex formation during T-cell activation. Primary T-cells or T-cell lines, such as Jurkat, are transfected with plasmids encoding for proteins of interest fused to monomeric fluorescent proteins, preventing non-physiological oligomerization 11. Live T cells are dropped over a coverslip pre-coated with T-cell activating antibody 8,9, which binds to the CD3/TCR complex, inducing T-cell activation while overcoming the need for specific activating antigens. Activated cells are constantly imaged with the use of confocal microscopy. Imaging data are analyzed to yield quantitative results, such as the colocalization coefficient of the signaling proteins.

We describe a live-cell imaging method that provides insight into protein dynamics during the T-cell activation process. We demonstrate the combined usage of the T-cell spreading assay, confocal microscopy and imaging analysis to yield quantitative results to follow signaling complex formation throughout T-cell activation.

Protection against infectious diseases is mediated by the immune system 1,2. T lymphocytes are the master coordinators of the immune system, regulating ...

Immunology and Infection

Engineering Intracellular Protein Sensors in Mammalian Cells

Proteins can function as biomarkers of pathological conditions, such as neurodegenerative diseases, infections or metabolic syndromes. Engineering cells to sense and respond to these biomarkers may help the understanding of molecular mechanisms underlying pathologies, as well as to develop new cell-based therapies. While several systems that detect extracellular proteins have been developed, a modular framework that can be easily re-engineered to sense different intracellular proteins was missing.
Here, we describe a protocol to implement a modular genetic platform that senses intracellular proteins and activates a specific cellular response. The device operates on intracellular antibodies or small peptides to sense with high specificity the protein of interest, triggering the transcriptional activation of output genes, through a TEV protease (TEVp)-based actuation module. TEVp is a viral protease that selectively cleaves short cognate peptides and is widely used in biotechnology and synthetic biology for its high orthogonality to the cleavage site. Specifically, we engineered devices that recognize and respond to protein-biomarkers of viral infections and genetic diseases, including mutated huntingtin, NS3 serine-protease, Tat and Nef proteins to detect Huntington&#8217;s disease, hepatitis C virus (HCV) and human immunodeficiency virus (HIV) infections, respectively. Importantly, the system can be hand tailored for the desired input-output functional outcome, such as fluorescent readouts for biosensors, stimulation of antigen presentation for immune response, or initiation of apoptosis to eliminate unhealthy cells.

Here, we present a protocol for engineering genetically-encoded intracellular protein sensor-actuator(s). The device specifically detects target proteins through intracellular antibodies (intrabodies) and responds by switching on gene transcriptional output. A general framework is built to rapidly replace intrabodies, enabling rapid detection of any desired protein, without altering the general architecture.

Proteins can function as biomarkers of pathological conditions, such as neurodegenerative diseases, infections or metabolic syndromes. Engineering cells ...

Culture and Transfection of Zebrafish Primary Cells

Zebrafish embryos are transparent and develop rapidly outside the mother, thus allowing for excellent in vivo imaging of dynamic biological processes in an intact and developing vertebrate. However, the detailed imaging of the morphologies of distinct cell types and subcellular structures is limited in whole mounts. Therefore, we established an efficient and easy-to-use protocol to culture live primary cells from zebrafish embryos and adult tissue.
In brief, 2 dpf zebrafish embryos are dechorionated, deyolked, sterilized, and dissociated to single cells with collagenase. After a filtration step, primary cells are plated onto glass bottom dishes and cultivated for several days. Fresh cultures, as much as long term differenciated ones, can be used for high resolution confocal imaging studies. The culture contains different cell types, with striated myocytes and neurons being prominent on poly-L-lysine coating. To specifically label subcellular structures by fluorescent marker proteins, we also established an electroporation protocol which allows the transfection of plasmid DNA into different cell types, including neurons. Thus, in the presence of operator defined stimuli, complex cell behavior, and intracellular dynamics of primary zebrafish cells can be assessed with high spatial and temporal resolution. In addition, by using adult zebrafish brain, we demonstrate that the described dissociation technique, as well as the basic culturing conditions, also work for adult zebrafish tissue.

We present an efficient and easy-to-use protocol for preparing primary cell cultures of zebrafish embryos for transfection and live cell imaging as well as a protocol to prepare primary cells from adult zebrafish brain.

Zebrafish embryos are transparent and develop rapidly outside the mother, thus allowing for excellent in vivo imaging of dynamic biological processes in ...

Mutagenesis and Functional Analysis of Ion Channels Heterologously Expressed in Mammalian Cells

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying potassium (referred to as GIRK) channels, which are important for regulating the excitability of neurons. There are four different mammalian GIRK channel subunits (GIRK1-GIRK4) - we focus on GIRK2 because it forms a homotetramer. Stimulation of different types of G protein-coupled receptors (GPCRs), such as the muscarinic receptor (M2R), leads to activation of GIRK channels. Alcohol also directly activates GIRK channels. We will show how to mutate one amino acid by specifically changing one or more nucleotides in the cDNA for the GIRK channel. This mutated cDNA sequence will be amplified in bacteria, purified, and the presence of the point mutation will be confirmed by DNA sequencing. The cDNAs for the mutated and wild-type GIRK channels will be transfected into human embryonic kidney HEK293T cells cultured in vitro. Lastly, whole-cell patch-clamp electrophysiology will be used to study the macroscopic potassium currents through the ectopically expressed wild-type or mutated GIRK channels. In this experiment, we will examine the effect of a L257W mutation in GIRK2 channels on M2R-dependent and alcohol-dependent activation.

We will demonstrate how to study the effect of a single point mutation on the function of an ion channel.

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying ...

Biology

Comparative Analysis of Forward and Reverse Transfection Methods on mPSCs

An Optimized Mouse Embryonic Stem Cell Based Reverse Poly-Transfection Technique for Rapid Exploration of Nucleic Acid Ratios

Due to its relative simplicity and ease of use, transient transfection of mammalian cell lines with nucleic acids has become a mainstay in biomedical research. While most widely used cell lines have robust protocols for transfection in adherent two-dimensional culture, these protocols often do not translate well to less-studied lines or those with atypical, hard-to-transfect morphologies. Using mouse pluripotent stem cells grown in 2i/LIF media, a widely used culture model for regenerative medicine, this method outlines an optimized, rapid reverse transfection protocol capable of achieving higher transfection efficiency. Leveraging this protocol, a three-plasmid poly-transfection is performed, taking advantage of the higher-than-normal efficiency in plasmid delivery to study an expanded range of plasmid stoichiometry. This reverse poly-transfection protocol allows for a one-pot experimental method, enabling users to optimize plasmid ratios in a single well, rather than across several co-transfections. By facilitating the rapid exploration of the effect of DNA stoichiometry on the overall function of delivered genetic circuits, this protocol minimizes the time and cost of embryonic stem cell transfection.

Author Spotlight: Advancements in Synthetic Genetic Devices for Stem Cell Fate Manipulation and Cellular Therapy Development

The present protocol describes a method for reverse poly-transfection of mouse embryonic stem cells during culture with 2i and LIF media. This method yields higher viability and efficiency than traditional forward transfection protocols, while also enabling one-pot optimization of plasmid ratios.

Due to its relative simplicity and ease of use, transient transfection of mammalian cell lines with nucleic acids has become a mainstay in biomedical ...

Use of Single Chain MHC Technology to Investigate Co-agonism in Human CD8+ T Cell Activation

Non-stimulatory self peptide MHC (pMHC) complexes do not induce T cell activation and effector functions, but can enhance T cell responses to agonist pMHC, through a process termed co-agonism. This protocol describes an experimental system to investigate co-agonism during human CD8+ T cell activation by expressing human MHC class I molecules presenting pre-determined peptides as single polypeptides (single chain MHC) in a xenogeneic cell line. We expressed single chain MHCs under conditions where low levels of agonist single chain p-MHC complexes and high levels of non-stimulatory single chain p-MHC complexes were expressed. Use of this experimental system allowed us to compare CD8+ T cell responses to agonist pMHC in the presence or absence of non-stimulatory pMHC. The protocol describes cell line transfection with single chain MHC constructs, generation of stable cell lines, culture of hepatitis B virus-specific human CD8+ T cells and T cell activation experiments simultaneously quantifying cytokine production and degranulation. The presented methods can be used for research on different aspects of CD8+ T cell activation in human T cell systems with known peptide MHC specificity.

This protocol describes the use of single chain MHC class I complexes to investigate molecular interactions in human CD8+ T cell activation: generation of engineered antigen presenting cells expressing single chain constructs, culture of human CD8+ T cell clone and T cell activation experiments.

Non-stimulatory self peptide MHC (pMHC) complexes do not induce T cell activation and effector functions, but can enhance T cell responses to agonist ...

Reliably Engineering and Controlling Stable Optogenetic Gene Circuits in Mammalian Cells

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.

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, ...