Name: experimental quantification of interactions between drug delivery systems and cells in vitro: a guide for preclinical nanomedicine evaluation
Uploaded: 2022-09-28T13:00:00
Description: Watch this Scientific Journal Video about Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation at JoVE.com

This work was supported by the Australian National Health and Medical Research Council (NHMRC; Program Grant No. GNT1149990), the Australian Centre for HIV and Hepatitis Virology Research (ACH2), as well a gift from the estate of R&#233;jane Louise Langlois. F.C. acknowledges the award of a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship (GNT1135806). Figure 1 and Figure 2 were created with BioRender.com.

1. Choosing the appropriate stream
<ol>
	<li>Follow the decision tree outlined in Figure 1 to determine the best workflow (stream) (Figure 2) for the experimental setup used. Refer to the discussion for further comments on this choice of stream.</li>
	<li>If following the Cytometer Stream, continue with steps 2.1.1-2.2.7. If following the Bulk Stream, continue with steps 3.1.1.1-3.1.5.7.</li>
</ol>
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<ol>
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(169), e62447 (2021).</li><li>Cui, 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=Immobilized+particle+imaging+for+quantification+of+nano-+and+microparticles.">Immobilized particle imaging for quantification of nano- and microparticles.</a> Langmuir. 32 (14), 3532-3540 (2016).</li><li>Shang, J., Gao, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nanoparticle+counting:+Towards+accurate+determination+of+the+molar+concentration.">Nanoparticle counting: Towards accurate determination of the molar concentration.</a> Chemical Society Reviews. 43 (21), 7267-7278 (2014).</li><li>Thomas, D. 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Characterizing the interactions between drug carriers and cells is becoming increasingly important in the development of novel drug delivery systems. Specifically, to allow the rational evaluation and comparison of various carrier constructs, absolute quantification of the performance of said carrier to interact with target and off-target cells is critical. This protocol describes a two-stream methodology that allows any researcher working with a drug carrier to convert relative, semiquantitative flow cytometry data on c...

The design of drug delivery constructs that exhibit specific, designed behavior depending on what cell type they encounter has attracted substantial research interest. Potential drug delivery constructs or &#34;carriers&#34; include lipid formulations, nano-grown inorganics, polymeric assemblies, extracellular vesicles, functionalized bacterial cells, or modified viruses. All of these can exhibit organ, tissue, or cell specificity due to physical properties, surface properties, or engineered chemical functionalizations such as antibody attachment1,2.
A nearly ubiquitous step in in vitro carrier evaluation is to incubate cells with a suspension containing said drug-loaded carrier. Post incubation, carrier performance is measured via a functional readout of the drug cargo&#39;s performance, for example, transfection efficiency or toxicity. Functional readouts are useful, as they are a downstream measure of carrier effectiveness. However, for more complex drug delivery constructs, it is increasingly important to move beyond functional readouts and separately quantify the degree of carrier interaction with the cell of interest. There are a few reasons for this.
First, there is increasing interest in discovering (and iteratively improving) &#34;platform&#34; carrier technologies, which can carry a variety of cargo. For example, lipid nanoparticles (LNPs) designed to encapsulate RNA can exchange one RNA sequence for another with few caveats3. Thus, to iteratively improve the carrier technology, it is critical to quantify its performance independent of the cargo functionality. Second, functional readouts may not be straightforward for the cargo of interest, compromising the ability to rapidly iterate and evaluate carrier formulations. While one could perform in vitro optimization using a model cargo with a straightforward functional readout (for instance, fluorescence), changing the cargo can change the biological response to a carrier4 and may, thus, not yield representative results. Third, many carriers are designed to interact with and be taken up by a specific cell type. Such targeting capability of a carrier can and should be differentiated from the performance of its therapeutic cargo&#160;post targeting. To continue the LNP example, an RNA cargo might be extremely potent, but if the LNP is unable to bind to the cell, be internalized, and release the RNA, no downstream functional effect will be observed. This can be an issue particularly for carriers intended to target hard-to-transfect cell types, such as T cells5. Conversely, an LNP could target extremely effectively, but the RNA cargo might not function. A downstream assay that just measures cargo functionality will be unable to differentiate between these two situations, thus complicating the development and optimization of carrier drug delivery systems.
In this work, how to absolutely quantify carrier association is discussed. Association is a term that refers to the experimentally measured degree of interaction between a carrier and a cell. Association does not differentiate between membrane binding and internalization-a carrier may be associated because it is bound to the cell surface or because the cell has internalized it. Association is commonly measured as part of cell-carrier incubation experiments. Historically, association has been reported either in arbitrary fluorescent units (typically &#34;median fluorescence intensity&#34; or MFI) or as &#34;percent association,&#34; metrics whose limitations have been previously discussed6. In short, these measurements are not comparable between experiments, laboratories, and drug carriers due to differences in experimental protocols, flow cytometer settings, and the labeling intensities of different carriers. Efforts have been made to overcome the former by calibrating the cytometer, thereby converting the relative measure of MFI into an absolutely quantitative measure of fluorescence7. However, this method does not account for the variability in the labeling intensity of various carriers and, thus, does not allow the rational comparison of various carrier performances in a target cell of choice8.
Here, how to practically convert from relative, arbitrary fluorescent units to the absolute quantitative metric of the &#34;number of carriers per cell&#34; is demonstrated by performing a small number of additional characterization experiments. If another metric of carrier concentration is desired (e.g., carrier mass per cell or carrier volume per cell), it is straightforward to convert from carriers per cell, provided carrier characterization has been done. For brevity and to avoid jargon, the word &#34;carrier&#34; is used within this work to refer to the vast assortment of drug delivery constructs. These quantification techniques are equally applicable, whether applied to a nano-engineered gold particle or a bio-engineered bacteria.
A few facts enable the conversion from arbitrary fluorescent units to carriers per cell. First, the measured fluorescence intensity is proportional to the concentration of a fluorophore9 (or a fluorescently labeled carrier), assuming the fluorescence is within the detection limits of the instrument and the instrumentation settings are the same. Thus, if the fluorescence of a carrier and the fluorescence of a sample are known, one can determine how many carriers are present in that sample if all the measurements were performed under the same settings and conditions. However, especially for smaller carriers, it may not be possible to measure carrier fluorescence, cell autofluorescence, and cell-associated-with-carriers fluorescence on the same instrument with the same settings. In this case, there is a second requirement to make it possible to convert between measured fluorescence on one instrument and measured fluorescence on another. To do so, a standard curve of fluorophore concentration can be established to measure the fluorescence intensity on both instruments, taking advantage of the Molecules of Equivalent Soluble Fluorochrome (MESF) standard9. This then allows measurement of the carrier fluorescence in bulk on a non-cytometer, a measurement that can be done on carriers of any size or characteristic. When such bulk quantification is done on a carrier suspension of known concentration, the number of carriers per cell of a sample can, once again, be calculated.
While this work demonstrates the process for measuring carrier association (as determined by measured fluorescence intensity), an analogous protocol could be performed for other measures of cell-carrier interaction (e.g., an experimental protocol that differentiates internalized and membrane-bound carriers). Additionally, this protocol would be largely the same if association was measured through a non-fluorescent assay (for instance, through mass cytometry).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Alexa Fluor 647 C2 Maleimide</td>
			<td>Invitrogen</td>
			<td>A20347</td>
			<td>pH-stable dye used to label 150 nm, 235 nm, or 633 nm PMASH carriers; example of good dye to use in cell-carrier association studies</td>
		</tr>
		<tr>
			<td>Apogee A50 Microflow</td>
			<td>Apogee</td>
			<td></td>
			<td>Sensitive flow cytometer capable of detecting small carriers for counting</td>
		</tr>
		<tr>
			<td>CytoFLEX S Flow Cytometer</td>
			<td>Beckman Coulter</td>
			<td></td>
			<td>Sensitive flow cytometer capable of detecting small carriers for counting and read out for final cell-barrier experiments</td>
		</tr>
		<tr>
			<td>FCS Express</td>
			<td>De Novo Software</td>
			<td></td>
			<td>Software used to analyze flow cytometry data, i.e., perform gating and derive median fluorescence intensity values of populations of choice. Alternatives include FlowJo, OMIQ, Python</td>
		</tr>
		<tr>
			<td>Infinite 200 PRO</td>
			<td>Tecan Lifesciences</td>
			<td></td>
			<td>Standard microplate reader instrument used for bulk fluorescence measurements of carriers in solution</td>
		</tr>
		<tr>
			<td>LSRFortessa Cell Analyzer</td>
			<td>BD Biosciences</td>
			<td></td>
			<td>Less sensitive flow cytometer, but one more generally available to researchers. Can be used to read out final cell-carrier experiment</td>
		</tr>
		<tr>
			<td>NanoSight NS300</td>
			<td>Malvern Panalytical</td>
			<td></td>
			<td>Instrument used for Nanoparticle Tracking Analysis</td>
		</tr>
		<tr>
			<td>Prism 8</td>
			<td>GraphPad</td>
			<td></td>
			<td>Software used to graph and calculate standard curves. Alternatives include Microsoft Excel, Origin, Minitab, Python amongst many others</td>
		</tr>
		<tr>
			<td>Quantum MESF kits Alexa Fluor 647</td>
			<td>Bangs Laboratories</td>
			<td>647</td>
			<td>Absolute quantitation beads for flow cytometery. Used to convert fluorescence intensities measured in bulk on a microplate reader to fluorescence intensities measured on a flow cytometer using the MESF standard</td>
		</tr>
	</tbody>
</table>

experimental quantification of interactions between drug delivery systems and cells in vitro: a guide for preclinical nanomedicine evaluation

A major component of designing drug delivery systems concerns how to amplify or attenuate interactions with specific cell types. For instance, a chemotherapeutic might be functionalized with an antibody to enhance binding to cancer cells (&#34;targeting&#34;) or functionalized with polyethylene glycol to help evade immune cell recognition (&#34;stealth&#34;). Even at a cellular level, optimizing the binding and uptake of a drug carrier is a complex biological design problem. Thus, it is valuable to separate how strongly a new carrier interacts with a cell from the functional efficacy of a carrier&#39;s cargo once delivered to that cell.
To continue the chemotherapeutic example, &#34;how well it binds to a cancer cell&#34; is a separate problem from &#34;how well it kills a cancer cell&#34;. Quantitative in vitro assays for the latter are well established and usually rely on measuring viability. However, most published research on cell-carrier interactions is qualitative or semiquantitative. Generally, these measurements rely on fluorescent labeling of the carrier and, consequently, report interactions with cells in relative or arbitrary units. However, this work can be standardized and be made absolutely quantitative with a small number of characterization experiments. Such absolute quantification is valuable, as it facilitates rational, inter- and intra-class comparisons of various drug delivery systems-nanoparticles, microparticles, viruses, antibody-drug conjugates, engineered therapeutic cells, or extracellular vesicles.
Furthermore, quantification is a prerequisite for subsequent meta-analyses or in silico modeling approaches. In this article, video guides, as well as a decision tree for how to achieve in vitro quantification for carrier drug delivery systems, are presented, which take into account differences in carrier size and labeling modality. Additionally, further considerations for the quantitative assessment of advanced drug delivery systems are discussed. This is intended to serve as a valuable resource to improve rational evaluation and design for the next generation of medicine.

As discussed previously, different drug carrier types require the use of different techniques for the absolute quantification of cell-carrier association. For example, 633 nm disulfide-stabilized poly(methacrylic acid) (PMASH) core-shell particles are large and dense enough for detection using a sensitive flow cytometer. As such, these particles were labeled fluorescently, then gated and counted using side-angle light scattering (SALS, analogous to SSC), as well as the appropriate fluorescent channel (<strong ...

Watch this Scientific Journal Video about Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation at JoVE.com

Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation

A workflow is demonstrated for the absolute quantification of drug carrier-cell interactions using flow cytometry to allow better rational evaluation of novel drug delivery systems. This workflow is applicable to drug carriers of any type.

Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation

A major component of designing drug delivery systems concerns how to amplify or attenuate interactions with specific cell types. For instance, a ...

This protocol provides quantitative data on how carrier systems interact with cells. Quantitative data is key for engineering and optimization. It allows us to move from yes or no biological questions, to how much questions.This technique is useful to convert results from preclinical drug carrier performance, which were qualitative, into quantitative results. And this works even when you characterize your carrier on a different system than you characterize your cells. It can take a few iterations to find the optimal carrier dilution that fall within the linear or quantitative ranges of the nano side.Take your time for finding the right conditions. To begin, mount the flow cell onto the laser module. Slowly flush the flow cell at the rate of 0.1 milliliter per second with one milliliter of distilled water.If bubbles form within the flow cell, partially retract the suspension to merge the bubble with the air-liquid interface before proceeding. Then lock the entire laser module in place inside the instrument. Start the camera roughly halfway through flushing.Be sure to confirm that the carrier debris is washed out. Select capture to open the capture settings tab and click start camera. Drive the system with one milliliter of air.If any static carriers are visible on the screen, clean the flow cell according to the manufacturer's instructions. Prepare the carriers for nanoparticle tracking analysis, by ensuring the carriers are well suspended via sonication or vortexing, depending on the carrier system involved. Dilute the carriers in water to prepare at least 0.6 to one milliliter of each sample with a carrier concentration between 1x10 to the seventh and 1x10 to the ninth carriers per milliliter.Take out the laser module and place it upright. Drop the first carrier sample into a one milliliter syringe and attach the syringe to the tube inlet. Then carefully load the sample into the flow cell.If bubbles form within the flow cell, retract the suspension partially to merge the bubble with the air-liquid interface before proceeding. Ensure the entire flow cell is filled with liquid, then pause the loading. Adjust the camera focus if needed to visualize individual carriers.Make coarse focus adjustments with the rotating knob on the right hand side of the instrument. Make finer adjustments by selecting the hardware tab. Change the focus by adjusting the focus slider.To ensure there is no oversaturation, select the optimal camera level by adjusting the slider within the capture tab. If the instrument is equipped with this accessory, load the syringe containing the carrier sample into the syringe pump. Under the SOP tab, select standard measurement to take five captures of 30 seconds each.Enter base file name, and if desired, add additional sample information by clicking the advanced button which will open a modal dialogue with various choices. Press create and run script and wait for a pop-up to appear asking to please advance sample. If using the syringe pump, select the hardware tab and then syringe pump tab.Set the desired infusion rate and press infuse. If not using the syringe pump, manually advance the sample. In the popup window, select OK to start capturing.After each of the five captures, when the please advanced sample pop up reappears, check that the sample is still moving through the flow cell. Then select OK to proceed with the next capture. In the process tab, adjust the detection threshold slider between four and eight to correctly identify distinct carriers visible on the screen.You can adjust the screen gain to aid visualization, it will not affect the downstream analysis. In the pop-up, press okay to initiate tracking analysis. Monitor the analysis progress by clicking on the analysis in single analysis tab.Once the analysis is finished, look for an export settings prompt to appear. Confirm that the include PDF and include experiment summary is selected. Select any other export formats as desired.In the results section of the PDF data export, to ensure the concentration measured is reliable, verify that the measured carrier concentration is between 1x10 to the seventh and 1x10 to the ninth carriers per milliliter and check for any error messages or messages of caution underneath the concentration measurement result. Repeat the procedure two or more times with different dilutions from the stock, and ensure that each sample's concentration falls within the instrument's linear range. Calculate the stock carrier concentration as described in the text manuscript.Prepare the free or antibody conjugated fluorochrome solution as described in the text manuscript. Calculate the concentration of the stock solution from the concentration, the molecular weight, and Avogadro's number as shown in the equation. Then perform a serial dilution of the dye in the carrier diluent to generate standard curve samples.Then add the carrier sample to the measurement plate and measure fluorescence using a microplate reader. Generate the standard curve as described in the text manuscript. Calculate the absolute fluorescence intensity per carrier by dividing the bulk fluorescence by the carrier concentration.Set up the flow cytometer for the final carrier cell experiment by determining optimal PMT voltage settings in the relevant channels. Run a negative control sample where the cells are not incubated with carriers to determine the background fluorescence. Prepare and re-suspend the flow cytometry quantitation beads.Use the same buffer as used for the cell samples. If the bead populations are provided separately, pull them together. Run the flow cytometry quantitation bead and the carrier cell samples to determine the fluorescence intensity per cell.Run the flow cytometer quantitation beads to generate a standard curve, converting the absolute fluorescent intensity into MFI. Use this to calculate the theoretical MFI of the carriers as described in the text manuscript. Run the cell carrier samples to determine the fluorescent intensity per cell of each sample.Finally, calculate the number of carriers per cell for each sample by subtracting the background fluorescence from the measured fluorescence of the sample, and then dividing by the fluorescence per particle. For 633 nanometer Polymethacrylic acid core shell particles, the concentration and fluorescent intensity per carrier were directly quantifiable using the cytometer stream. In contrast, 100 nanometer super paramagnetic iron oxide nanoparticles were too small to detect individually and were analyzed using the bulk stream.Labeled quantitation beads were used on multiple days to generate standard curves on a flow cytometer. The correlation between the measured MFI and the MESF value of the quantitation beads was linear and broadly similar between the measured dates. Time course experiments using fluorescent labeled HeLa cells with 235 nanometer polymethacrylic acid capsules from zero to 24 hours showed an increase in the MFI over time, indicating the capsules were associating with HeLa cells.The difference in the apparent cellular response to carriers was stark, depending on relative or absolute quantification. The absolute quantification was independent of labeling intensity and thus more comparable. These techniques are the foundation for deeper analysis and comparison of particle performance.We're excited about using these techniques to parameterize mathematical models which will allow us to characterize particle performance independent of any particular experiment.

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