This work was supported by core budget from the p53lab (BMSI, A*STAR) and the JCO VIP grant (A*STAR).

HEK293 cell line was used for the protocol and was cultured in Dulbecco&#39;s Modified Eagle Medium supplemented with 10% Fetal Bovine Serum, 2 mM L-glutamine, and 100 U/mL penicillin/streptomycin. Cells were cultured at 37 &#176;C with 5% CO2 in a humidified environment.
1. Quantitative assessment of eIF4F complex disruption via eIF4E:eIF4G604-646 complementation assay
<ol>
	<li>Cell culture and transient transfection of eIF4E:eIF4G604-646 complementation assay
	<ol>
		<li>Use freshly thawed cells with less than 20 passages for all experiments. On day 1, determine the cell number using a standard cell counter and count the total viable cells using the Trypan blue exclusion method13.</li>
		<li>Seed 6-well plates with 0.9 - 1.2 x 106 of HEK 293 cells per well in 2 mL of standard growth medium. 
		NOTE: In order to achieve the best transfection efficiency within the cell lines indicated above, ensure that the plated cells are 70-90% confluent the day after seeding.</li>
		<li>On the morning of day 2, co-transfect cells with SubA-eIF4E and eIF4G604-646-SubB plasmid using a lipid-based transfection reagent (see Table of Materials) as described below.
		<ol>
			<li>Dilute 9 &#181;L of liposome-based solution in a tube containing 125 &#181;L of reduced serum medium without phenol red (see Table of Materials) for each transfection and incubate at room temperature for 5 min.</li>
			<li>Prepare master mix of DNA by diluting 3 &#181;g of each plasmid in 125 &#181;L of reduced serum medium for each transfection tube.</li>
			<li>Add 12 &#181;L of enhancer reagents to the DNA master mix tube, mix well and immediately add the DNA: enhancer master mix to each tube of diluted liposomes in a ratio 1:1. Incubate for 15 min at room temperature.</li>
			<li>Add the DNA-lipid complex to each well and incubate the cells in a 37 &#176;C incubator with 5% CO2 for 24 h.</li>
		</ol>
		</li>
		<li>On the morning of day 3, rinse each well with 1 mL of PBS.</li>
		<li>Remove the PBS and incubate cells in each well with 0.3 mL of trypsin for 5 min at 37 &#176;C.</li>
		<li>Neutralize trypsin by adding 2 mL of the reduced serum medium without phenol red containing 0% FCS in each well and transfer transfected cells into a 15 mL tube. 
		NOTE: Phenol red can interfere with the luciferase activity; therefore, cells must be handled from this step onward in medium without phenol red.</li>
		<li>Spin down cells for 5 min at 290 x g and aspirate the medium. Resuspend the cells in 2 mL of the reduced serum medium without phenol red containing 0% FCS. Count as described in step 1.1.</li>
		<li>Seed transfected HEK 293 cells in 96 well opaque plates at a density of 30,000 cells per well in 90 &#181;L of medium without phenol red containing 0% FCS. In order to obtain 3 technical replicates within the same experiment for 3 different compounds, seed 60 wells of the plates with cells excluding the wells on the edges.</li>
		<li>Immediately after seeding the transfected cells, add 10 &#181;L of 10% DMSO compound solution (see step 1.2).</li>
	</ol>
	</li>
	<li>Compound preparation
	<ol>
		<li>Prepare 1 mM compound stock solutions by dissolving the compound of interest in 100% DMSO (v/v). In order to have 3 replicates for each compound titration, use 8 &#181;L of the 1 mM compound stock solution. 
		NOTE: The volume of 1 mM stock compound used is more than what it is needed. This is done to take into account pipetting errors if any.</li>
		<li>Perform a 2-fold serial dilution of the stock compound solution with 4 &#181;L of the 1 mM stock into 4 &#181;L of 100% DMSO for each titration point. 
		NOTE: For a complete compound titration, perform 9 serial dilutions from the starting stock in 100% DMSO. If multiple compounds need to be tested, use a 96 well plate and a multichannel pipette to facilitate this step and subsequent dilutions.</li>
		<li>Add 36 &#181;L of HPLC grade sterile water for each point of the 2-fold serial dilution to prepare a 40 &#181;L of 10x working solutions in 10% DMSO (v/v) (i.e. 100 &#956;M to 0.39 &#956;M 10% DMSO stock series will lead to a final solution of 10 &#956;M to 0.039 &#956;M,in 1% DMSO when added to cells). As for treatment control, also prepare a 10% DMSO only stock solution in HPLC grade sterile water.</li>
		<li>Add 10 &#181;L of 10x working solutions to the cells in the 96 well opaque plate in order to yield the intended final concentration with a residual DMSO concentration of 1% (v/v) in a total volume of 100 &#181;L and incubate for 3 h at 37 &#176;C with 5% v/v atmospheric CO2.</li>
	</ol>
	</li>
	<li>Luciferase complementation and viability assay
	<ol>
		<li>After 3 h of drugging, start preparing the luciferase substrate reagent by combining 1 volume of substrate with 19 volumes of the dilution reagent (see manufacturer&#39;s instructions). 
		NOTE: The luciferase assay is performed using a commercially available kit (see Table of Materials).</li>
		<li>Use a multichannel pipette to immediately add 25 &#181;L of substrate reagent.</li>
		<li>Shake the plate at 350 rpm for 50 min on an orbital shaker at room temperature.</li>
		<li>Assess luminescence using a plate reader. To do so, set the mirror reader on luminescence and the emission filter on 455. Use a measurement height of 6.5 mm with a measurement time of 1 s.</li>
		<li>In order to asses cell viability, add 33 &#181;L of viability assay reagent and then re-measure luminescence again after 15 min at room temperature, using the plate reader.</li>
		<li>Assess luminescence with the plate reader by setting the mirror reader on Luminescence and the emission filter on 600 nm. Use a measurement height of 6.5 mm with a measurement time of 1 s. 
		NOTE: Viability is assessed by measuring the intracellular level of ATP upon cell lysis as per manufacturer instructions. Multiplexing is possible in this case since the viability assay uses a different luciferase with a different emission wavelength.</li>
		<li>Use the data to determine the IC50 values of each compound by curve fitting the data to a 4 parameter fitting curve equation: 
		Y = ((A-D)/(1+((x/C)^B))) + D 
		The numerical values D and A must be constrained in the curve fitting process: 
		D = luminescent value on the Y-axis for minimal curve asymptote or minimal theoretical level of response expected from the eIF4E:4G complementation assay. 
		A = luminescent value on the Y-axis for maximal curve asymptote or maximal theoretical level response expected from the eIF4E:4G complementation assay. 
		NOTE: The value for the minimal response is derived from the 1% DMSO (v/v) control treatment and the value for the maximal response is derived from the maximal response of a high affinity mTOR inhibitor that has plateaued with no effect on cell viability.</li>
	</ol>
	</li>
</ol>
2. Correlating eIF4E:eIF4G604-646 assay inhibition with eIF4F complex disruption in cells
<ol>
	<li>To confirm that the signal being measured by the assay corresponds to the physical disruption of eIF4E-eIF4G interaction by the compound, seed cells as described in step 1.1.</li>
	<li>The day after, replace the medium with 1 mL of reduced serum medium not containing phenol red and incubate for 4 h with the compound of interest. Ensure residual DMSO concentration of 1% (v/v) in a total volume of 1 mL. 
	NOTE: In order to easily allow a correlation with the result, use compound concentrations at the beginning, midpoint and endpoint of the measured complementation assay titration curve.</li>
	<li>Lyse cells and performed m7GTP pull down to isolate eIF4F and eIF4E:4EBP1 complexes. Detailed procedures on how to perform the m7GTP pull down experiment can be found elsewhere14.</li>
	<li>Detect m7GTP bound eIF4G, eIF4E and 4EBP1 protein levels by western blot analysis, and then correlate to eIF4F complex disruption with complementation assay signal inhibition.</li>
</ol>

<ol>
	<li>Silvera, D., Formenti, S. C., Schneider, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translational+control+in+cancer.">Translational control in cancer.</a> Nature Reviews Cancer. 10 (4), 254-266 (2010).</li><li>Gebauer, F., Hentze, M. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+Mechanism+of+translational+control.">Molecular Mechanism of translational control.</a> Nature Reviews Molecular Cell Biology. 10, 827-835 (2004).</li><li>Bhat, M., 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=Targeting+the+translation+machinery+in+cancer.">Targeting the translation machinery in cancer.</a> Nature Reviews Drug Discovery. 14 (4), 261-278 (2015).</li><li>Pelletier, J., Graff, J., Ruggero, D., Sonenberg, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+the+eIF4F+translation+initiation+complex:+a+critical+nexus+for+cancer+development.">Targeting the eIF4F translation initiation complex: a critical nexus for cancer development.</a> Cancer Research. 75 (2), 250-263 (2015).</li><li>Montanaro, L., Pandolfi, P. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Initiation+of+mRNA+translation+in+oncogenesis:+the+role+of+eIF4E.">Initiation of mRNA translation in oncogenesis: the role of eIF4E.</a> Cell Cycle. 11, 1387-1389 (2004).</li><li>Merrick, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=eIF4E:+A+retrospective.">eIF4E: A retrospective.</a> Journal of Biological Chemistry. 290 (40), 24091-24099 (2015).</li><li>Marcotrigiano, J., Gingras, A. C., Sonenberg, N., Burley, S. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cap-dependent+translation+initiation+in+eukaryotes+is+regulated+by+a+molecular+mimic+of+eIF4G.">Cap-dependent translation initiation in eukaryotes is regulated by a molecular mimic of eIF4G.</a> Molecular Cell. 3 (6), 707-716 (1999).</li><li>Umenaga, Y., Paku, K. S., In, Y., Ishida, T., Tomoo, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+and+function+of+the+second+eIF4E-binding+region+in+N-terminal+domain+of+eIF4G:+comparison+with+eIF4E-binding+protein.">Identification and function of the second eIF4E-binding region in N-terminal domain of eIF4G: comparison with eIF4E-binding protein.</a> Biochemical and Biophysical Research Communication. 414 (3), 462-467 (2011).</li><li>Gr&#252;ner, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Structures+of+eIF4E-eIF4G+Complexes+Reveal+an+Extended+Interface+to+Regulate+Translation+Initiation.">The Structures of eIF4E-eIF4G Complexes Reveal an Extended Interface to Regulate Translation Initiation.</a> Molecular Cell. 64 (3), 467-479 (2016).</li><li>Gingras, A. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hierarchical+phosphorylation+of+the+translation+inhibitor+4E-BP1.">Hierarchical phosphorylation of the translation inhibitor 4E-BP1.</a> Genes and Development. 15 (21), 2852-2864 (2001).</li><li>Dixon, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NanoLuc+Complementation+Reporter+Optimized+for+Accurate+Measurement+of+Protein+Interactions+in+Cells.">NanoLuc Complementation Reporter Optimized for Accurate Measurement of Protein Interactions in Cells.</a> ACS Chemical Biology. 11 (2), 400-408 (2016).</li><li>Sun, S. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=mTOR+kinase+inhibitors+as+potential+cancer+therapeutic+drugs.">mTOR kinase inhibitors as potential cancer therapeutic drugs.</a> Cancer Letters. 340 (1), 1-8 (2013).</li><li>Strober, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trypan+blue+exclusion+test+of+cell+viability.">Trypan blue exclusion test of cell viability.</a> Current Protocols in Immunology. , (2001).</li><li>Sekiyama, N., 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=Molecular+mechanism+of+the+dual+activity+of+4EGI-1:+Dissociating+eIF4G+from+eIF4E+but+stabilizing+the+binding+of+unphosphorylated+4E-BP1.">Molecular mechanism of the dual activity of 4EGI-1: Dissociating eIF4G from eIF4E but stabilizing the binding of unphosphorylated 4E-BP1.</a> Proceedings of the National Academy of Science U. S. A. 112 (30), e4036-e4045 (2015).</li><li>Muller, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=4E-BP+restrains+eIF4E+phosphorylation.">4E-BP restrains eIF4E phosphorylation.</a> Translation. 1 (2), e25819 (2013).</li><li>Frosi, Y., Usher, R., Lian, D. T. G., Lane, D. P., Brown, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+flux+in+signalling+pathways+through+measurements+of+4EBP1-mediated+eIF4F+complex+assembly.">Monitoring flux in signalling pathways through measurements of 4EBP1-mediated eIF4F complex assembly.</a> BMC Biology. (1), 40 (2019).</li><li>Ran, X., Gestwicki, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibitors+of+protein-protein+interactions+(PPIs):+An+analysis+of+scaffold+choices+and+buried+surface+area.">Inhibitors of protein-protein interactions (PPIs): An analysis of scaffold choices and buried surface area.</a> Current Opinion in Chemical Biology. 44, 75-86 (2018).</li></ol>

The authors have nothing to disclose.

The method described in this article utilizes a luciferase-based complementation assay to quantitatively monitor eIF4F complex assembly through direct measurement of eIF4G-eIF4E interaction in live cells. We have provided details for use of eIF4E-eIF4G complementation system and we have also showed that the system is extremely accurate in measuring drug-induced 4EBP1-mediated dissociation of eIF4E-eIF4G interaction16. In order to facilitate the throughput of this&#160;assay, the experimental setup...

Control of gene expression plays a pivotal role in the correct execution of cellular programs such as growth proliferation and differentiation. A regulatory control mechanism can be exerted either at the level of gene transcription or at the level of mRNA translation. In the last decade, it has become increasingly evident that translational control by modulation of the initiation process rather than the later steps of elongation and termination can finely regulate synthesis of specific subsets of proteins that play a wide range of biological functions.
Increased translation of mRNAs involved in survival, anti-autophagic and anti-apoptotic responses have been implicated in several cancers and have also been causatively linked to either aberrant activation or over expression of translation initiation factors1.
The eIF4F complex is a master regulator of translation initiation. By binding the cap-structure on the 5&#39; end of mRNAs, eIF4F is driving initial mRNA-ribosome recruitment and in turn increasing mRNA translation efficiency of weakly translated eukaryotic mRNAs2. eIF4F mediated translation of cancer-related mRNAs has been reported for many cancer models harboring aberrant activation of RAS/MAPK or AKT/TOR pathways, suggesting that cancer cells upregulate eIF4F to boost their own pro-neoplastic activity. Disruption of this feed-forward loop by inhibiting eIF4F complex formation is thereby a very promising therapeutic strategy3,4.
The eIF4F complex consists of (i) eIF4E, the cap-binding subunit of eIF4F that interacts with the cap structure found at the 5&#39; UTR of mRNA, (ii) eIF4A, the RNA helicase and (iii) eIF4G, the scaffold protein that interacts with both eIF4A and eIF4E and which eventually recruits the 40S ribosomal subunit5. eIF4G association with eIF4E is the rate-limiting step for the assembly of functional eIF4F complexes and it is negatively regulated by the eIF4E binding proteins (4EBPs, members 1, 2 and 3))6. By competing with eIF4G binding to eIF4E through an interface that consists of canonical and non-canonical eIF4E binding sequences7,8,9 (region spanning aa 604-646 on human eIF4E), 4EBP reduces the pool of eIF4E actively involved in translation and preventing eIF4F complex formation. Interplay of these protein-protein interactions is mainly regulated by the mammalian target of rapamycin (mTOR)-mediated phosphorylation of 4EBP. Upon mitogenic stimuli, mTOR directly phosphorylates the members of the 4E-BP protein family, decreasing their association with eIF4E and, thereby, promoting eIF4E-eIF4G interaction and formation of functional eIF4F complexes10.
Despite the great effort in developing compounds targeting eIF4F complex integrity, the lack of assays measuring direct disruption of eIF4E-eIF4G interaction in live cells has limited the search for cellular active hit compounds. We have applied a luciferase assay based on a coelenterazine analog (e.g., Nanoluc-based complementation assay) to monitor in real time the status of eIF4F integrity through the eIF4E-eIF4G interaction. The luciferase complementation protein system consists of an 18 kDa protein fragment (SubA) and 11 amino acid peptide fragment (SubB) optimized for minimal self-association and stability11.&#160;Once expressed as a fusion product with the human full length eIF4E and the eIF4E interaction domain from human eiF4G1 (aa 604-646), the two interacting proteins will bring the SubA and SubB fragment into close proximity of each other and will induce the formation of the active luciferase that, in presence of a cell permeable substrate, will eventually generate a bright luminescent signal (Figure 1). We have reported elsewhere the construction and validation of the eIF4E:eIF4G604-646 complementation system16.
Here, we describe how the eIF4E:eIF4G604-646 complementation system (available upon request) can be applied to accurately measure 4EBP1-mediated eIF4E-eIF4G disruption in live cells. Additionally, we demonstrate its utility by measuring the effects of several mTOR inhibitors that are currently under clinical trials as potential cancer therapeutic drugs12. Because off-target&#160;effects often mask drug-specific activity, we also describe how the versatility of the eIF4E:eIF4G604-646 system measurement can be extended with orthogonal measurements of cellular viability to take these into account.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>293FT cells</td>
			<td>Thermo Fisher Scientific</td>
			<td>R70007</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell culture microplate 96 well, F-Bottom</td>
			<td>greiner bio-one</td>
			<td>655083</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell titer Glo 2.0</td>
			<td>PROMEGA</td>
			<td>G9241</td>
			<td></td>
		</tr>
		<tr>
			<td>Envision Multilabel Reader</td>
			<td>PerkinElmer</td>
			<td>not applcable</td>
			<td></td>
		</tr>
		<tr>
			<td>Finnpipette F2 Multichannel Pipettes 12-channels 30-300 ml</td>
			<td>Thermo Fisher Scientific</td>
			<td>4662070</td>
			<td></td>
		</tr>
		<tr>
			<td>Finnpipette F2 Multichannel Pipettes 12-channels 5-50 ml</td>
			<td>Thermo Fisher Scientific</td>
			<td>4662050</td>
			<td></td>
		</tr>
		<tr>
			<td>FUGENE6</td>
			<td>PROMEGA</td>
			<td>E2692</td>
			<td></td>
		</tr>
		<tr>
			<td>Lipofectamine 3000</td>
			<td>Thermo Fisher Scientific</td>
			<td><a href="https://www.thermofisher.com/order/catalog/product/L3000015" target="_blank">L3000015</a></td>
			<td></td>
		</tr>
		<tr>
			<td>NanoBiT PPI Starter Systems</td>
			<td>PROMEGA</td>
			<td>N2014</td>
			<td></td>
		</tr>
		<tr>
			<td>Optimem I Reduced Serum Mediun, no phenol red</td>
			<td>Thermo Fisher Scientific</td>
			<td>11058021</td>
			<td></td>
		</tr>
		<tr>
			<td>Orbital shaker</td>
			<td>Eppendorf</td>
			<td>not appicable</td>
			<td></td>
		</tr>
		<tr>
			<td>&#947;-Aminophenyl-m7GTP (C10-spacer)-Agarose</td>
			<td>Jena Bioscience</td>
			<td>AC-155S</td>
			<td></td>
		</tr>
	</tbody>
</table>

monitoring eif4f assembly by measuring eif4e-eif4g interaction in live cells

Formation of the eIF4F complex has been shown to be the key downstream node for the convergence of signalling pathways that often undergo&#160;oncogenic activation in humans. eIF4F is a cap-binding complex involved in the mRNA-ribosome recruitment phase of translation initiation. In many cellular and pre-clinical model of cancers, the deregulation of eIF4F leads to increased translation of specific mRNA subsets that are involved in cancer proliferation and survival. eIF4F is a hetero-trimeric complex built from the cap-binding subunit eIF4E, the helicase eIF4A and the scaffolding subunit eIF4G. Critical for the assembly of active eIF4F complexes is the protein-protein interaction between eIF4E and eIF4G proteins. In this article, we describe a protocol to measure eIF4F assembly that monitors the status of eIF4E-eIF4G interaction in live cells. The eIF4e:4G cell-based protein-protein interaction assay also allows drug induced changes in eIF4F complex integrity to be accurately and reliably assessed. We envision that this method can be applied for verifying the activity of commercially available compounds or for further screening of novel compounds or modalities that efficiently disrupt formation of eIF4F complex.

In order to validate the sensitivity of the eIF4E:eIF4G604-646 complementation system, 4EBP1 mediated inhibition of eIF4F complex assembly was assessed by using mTOR inhibitors. By inhibiting mTORC1 kinase dependent phosphorylation of the 4EBP protein family, mTOR inhibition enhances 4EBP1 association to eIF4E and, therefore, eIF4F disassembly15. Two classes of mechanistically different inhibitor of mTOR kinases were tested: rapalogs (e.g., Rapamycin) th...

Watch this Scientific Journal Video about Monitoring eIF4F Assembly by Measuring eIF4E-eIF4G Interaction in Live Cells at JoVE.com

Monitoring eIF4F Assembly by Measuring eIF4E-eIF4G Interaction in Live Cells

Here, we present a protocol to measure eIF4E-eIF4G interaction in live cells that would enable the user to evaluate drug induced perturbation of eIF4F complex dynamics in screening formats.

Formation of the eIF4F complex has been shown to be the key downstream node for the convergence of signalling pathways that often undergo&#160;oncogenic ...

monitoring-eif4f-assembly-measuring-eif4e-eif4g-interaction-live

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Cancer Research

3.0K Views.  A*STAR (Agency for Science, Technology and Research). Here, we present a protocol to measure eIF4E-eIF4G interaction in live cells that would enable the user to evaluate drug induced perturbation of eIF4F complex dynamics in screening formats.

Video: Monitoring eIF4F Assembly by Measuring eIF4E-eIF4G Interaction in Live Cells

Live Imaging to Study Microtubule Dynamic Instability in Taxane-resistant Breast Cancers

Taxanes such as docetaxel belong to a group of microtubule-targeting agents (MTAs) that are commonly relied upon to treat cancer. However, taxane resistance in cancerous cells drastically reduces the effectiveness of the drugs' long-term usage. Accumulated evidence suggests that the mechanisms underlying taxane resistance include both general mechanisms, such as the development of multidrug resistance due to the overexpression of drug-efflux proteins, and taxane-specific mechanisms, such as those that involve microtubule dynamics. 
Because taxanes target cell microtubules, measuring microtubule dynamic instability is an important step in determining the mechanisms of taxane resistance and provides insight into how to overcome this resistance. In the experiment, an in vivo method was used to measure microtubule dynamic instability. GFP-tagged &#945;-tubulin was expressed and incorporated into microtubules in MCF-7 cells, allowing for the recording of the microtubule dynamics by time lapse using a sensitive camera. The results showed that, as opposed to the non-resistant parental MCF-7CC cells, the microtubule dynamics of docetaxel-resistant MCF-7TXT cells are insensitive to docetaxel treatment, which causes the resistance to docetaxel-induced mitotic arrest and apoptosis. This paper will outline this in vivo method of measuring microtubule dynamic instability.

In this paper, we report a protocol describing an in vivo method to measure microtubule dynamic instability in docetaxel-resistant breast cancer cells (MCF-7TXT). In this method, a deconvolution microscopy imaging system is used to detect the expression of GFP-tubulin in target cells.

Taxanes such as docetaxel belong to a group of microtubule-targeting agents (MTAs) that are commonly relied upon to treat cancer. However, taxane ...

A Spheroid Killing Assay by CAR T Cells

Immunotherapy has become a field of growing interest in the fight against cancer otherwise untreatable. Among all immunotherapeutic methods, chimeric antigen receptor (CAR) redirected T cells obtained the most spectacular results, in particular with pediatric B-acute lymphoblastic leukemia (B-ALL). Classical validation methods of CAR T cells rely on the use of specificity and functionality assays of the CAR T cells against target cells in suspension and in xenograft models. Unfortunately, observations made in vitro are often decoupled from results obtained in vivo and a lot of effort and animals could be spared by adding another step: the use of 3D culture. The production of spheroids out of potential target cells that mimic the 3D structure of the tumor cells when they are engrafted into the animal model represents an ideal alternative. Here, we report an affordable, reliable and easy method to produce spheroids from a transduced colorectal cell line as a validation tool for adoptive cell therapy (exemplified here by CD19 CAR T cells). This method is coupled with an advanced live imaging system that can follow spheroid growth, effector cells cytotoxicity and tumor cell apoptosis.

This protocol is designed to assess immunotherapeutic redirected T-cell (CAR T-cell) cytotoxicity against 3D structured cancerous cells (spheroids) in real time.

Immunotherapy has become a field of growing interest in the fight against cancer otherwise untreatable. Among all immunotherapeutic methods, chimeric ...

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular sub-classification and temporal monitoring of tumor response to therapy. Here, we describe our dPCR-based method for the detection of tumor somatic mutations in cell free DNA (cfDNA), readily available in&#160;patient biofluids. Although limited in the number of mutations that can be tested for in each assay, this method provides a high level of sensitivity and specificity. Monitoring of mutation abundance, as calculated by MAF, allows for the evaluation of tumor response to therapy, thereby providing a much-needed supplement to radiographic imaging.

Here, we present a protocol to detect tumor somatic mutations in circulating DNA present in patient biological fluids (biofluids). Our droplet digital polymerase chain reaction (dPCR)-based method enables quantification of the tumor mutation allelic frequency (MAF), facilitating a minimally invasive complement to diagnosis and temporal monitoring of tumor response.

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular ...

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL of blood) warrant a poor prognosis and are correlated with shorter overall survival in several cancers (e.g., breast, prostate and colorectal). Currently, the anti-EpCAM-coated magnetic bead-based CTC capturing system is the gold standard test approved by the U.S. Food and Drug Administration (FDA) for enumerating CTCs in the bloodstream. This test is based on the use of magnetic beads coated with anti-EpCAM markers, which specifically target epithelial cancer cells. Many studies have illustrated that EpCAM is not the optimal marker for CTC detection. Indeed, CTCs are a heterogeneous subpopulation of cancer cells and are able to undergo an epithelial-to-mesenchymal transition (EMT) associated with metastatic proliferation and invasion. These CTCs are able to reduce the expression of cell surface epithelial marker EpCAM, while increasing mesenchymal markers such as vimentin. To address this technical hurdle, other isolation methods based on physical properties of CTCs have been developed. Microfluidic technologies enable a label-free approach to CTC enrichment from whole blood samples. The spiral microfluidic technology uses the inertial and Dean drag forces with continuous flow in curved channels generated within a spiral microfluidic chip. The cells are separated based on the differences in size and plasticity between normal blood cells and tumoral cells. This protocol details the different steps to characterize the programmed death-ligand 1 (PD-L1) expression of CTCs, combining a spiral microfluidic device with customizable immunofluorescence (IF) marker set.

The characterization of circulating tumor cells (CTCs) is a popular topic in translational research. This protocol describes a semi-automatic immunofluorescence (IF) assay for PD-L1 characterization and enumeration of CTCs in non-small cell lung cancer (NSCLC) patient samples.

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL ...

In Vitro Assay to Study Tumor-macrophage Interaction

Tumor associated macrophages (TAMs) account for a large percentage of cells in the tumor mass for different types of cancers. Glioblastoma (GBM), a malignant brain tumor with no cure, has up to a half the tumor mass TAMs. TAMs can be pro-tumoral or anti-tumoral, depending on the activation of specific genes in the cells. Genetic mutations in the tumors, through regulating cytokine expression, can affect recruitment of TAMs to the tumor microenvironment. Here, we describe a quantitative cell-based assay to assess macrophage recruitment by the conditioned medium from the tumor cells. This assay uses the human macrophage cell line MV-4-11 to study macrophage attraction by the conditioned medium from glioblastoma, allowing for high reproducibility and low variability. Data generated with this assay can contribute to a better understanding of the interaction between the tumor and the tumor microenvironment. Similar assay can be used to assess interaction between the tumor cells and other immune cells, including T cells and natural killer (NK) cells.

This article represents a useful in vitro assay to evaluate the capability of conditioned medium from tumor cells to attract macrophages.

Tumor associated macrophages (TAMs) account for a large percentage of cells in the tumor mass for different types of cancers. Glioblastoma (GBM), a ...

Monitoring Cancer Cell Invasion and T-Cell Cytotoxicity in 3D Culture

Significant progress has been made in treating cancer with immunotherapy, although a large number of cancers remain resistant to treatment. A limited number of assays allow for direct monitoring and mechanistic insights into the interactions between tumor and immune cells, amongst which, T-cells play a significant role in executing the cytotoxic response of the adaptive immune system to cancer cells. Most assays are based on two-dimensional (2D) co-culture of cells due to the relative ease of use but with limited representation of the invasive growth phenotype, one of the hallmarks of cancer cells. Current three-dimensional (3D) co-culture systems either require special equipment or separate monitoring for invasion of co-cultured cancer cells and interacting T-cells.
Here we describe an approach to simultaneously monitor the invasive behavior in 3D of cancer cell spheroids and T-cell cytotoxicity in co-culture. Spheroid formation is driven by enhanced cell-cell interactions in scaffold-free agarose microwell casts with U-shaped bottoms. Both T-cell co-culture and cancer cell invasion into type I collagen matrix are performed within the microwells of the agarose casts without the need to transfer the cells, thus maintaining an intact 3D co-culture system throughout the assay. The collagen matrix can be separated from the agarose cast, allowing for immunofluorescence (IF) staining and for confocal imaging of cells. Also, cells can be isolated for further growth or subjected to analyses such as for gene expression or fluorescence activated cell sorting (FACS). Finally, the 3D co-culture can be analyzed by immunohistochemistry (IHC) after embedding and sectioning. Possible modifications of the assay include altered compositions of the extracellular matrix (ECM) as well as the inclusion of different stromal or immune cells with the cancer cells.

The presented approach simultaneously evaluates cancer cell invasion in 3D spheroid assays and T-cell cytotoxicity. Spheroids are generated in a scaffold-free agarose multi-microwell cast. Co-culture and embedding in type I collagen matrix are performed within the same device which allows to monitor cancer cell invasion and T-cell mediated cytotoxicity.

Significant progress has been made in treating cancer with immunotherapy, although a large number of cancers remain resistant to treatment. A limited ...

In Vitro Evaluation of Oncogenic Transformation in Human Mammary Epithelial Cells

Tumorigenesis is a multi-step process in which cells acquire capabilities&#160;that allow their growth, survival, and dissemination under hostile conditions. Different tests seek to identify and quantify these hallmarks of cancerous cells; however, they often focus on a single aspect of cellular transformation and, in fact, multiple tests are required for their proper characterization. The purpose of this work is to provide researchers with a set of tools to assess cellular transformation in vitro from a broad perspective, thereby making it possible to draw sound conclusions.
A sustained proliferative signaling activation is the major feature of tumoral tissues and can be easily monitored under in vitro conditions by calculating the number of population doublings achieved over time. Besides, the growth of cells in 3D cultures allows their interaction with surrounding cells, resembling what occurs in vivo. This enables the evaluation of cellular aggregation and, together with immunofluorescent labeling of distinctive cellular markers, to obtain information on another relevant feature of tumoral transformation: the loss of proper organization. Another remarkable characteristic of transformed cells is their capacity to grow without attachment to other cells and to the extracellular matrix, which can be evaluated with the anchorage assay.
Detailed experimental procedures to evaluate cell growth rate, to perform immunofluorescent labeling of cell lineage markers in 3D cultures, and to test anchorage-independent cell growth in soft agar are provided. These methodologies are optimized for Breast Primary Epithelial Cells (BPEC) due to its relevance in breast cancer; however, procedures can be applied to other cell types after some adjustments.

This protocol provides experimental in vitro tools to evaluate the transformation of human mammary cells. Detailed steps to follow-up cell proliferation rate, anchorage-independent growth capacity, and distribution of cell lineages in 3D cultures with basement membrane matrix are described.

Tumorigenesis is a multi-step process in which cells acquire capabilities&#160;that allow their growth, survival, and dissemination under hostile ...

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

The aggressiveness and lack of well-tolerated and widely effective treatments for advanced hepatocellular carcinoma (HCC), the predominant form of liver cancer, rationalize its rank as the second most common cause of cancer-related death. Preclinical models need to be adapted to recapitulate the human conditions to select the best therapeutic candidates for clinical development and aid the delivery of personalized medicine. Three-dimensional (3D) cellular spheroid models show promise as an emerging in vitro alternative to two-dimensional (2D) monolayer cultures. Here, we describe a 3D tumor spheroid&#160;model which exploits the ability of individual cells to aggregate when maintained in hanging droplets, and is more representative of an in vivo environment than standard monolayers. Furthermore, 3D spheroids can be produced by combining homotypic or heterotypic cells, more reflective of the cellular heterogeneity in vivo, potentially enabling the study of environmental interactions that can influence progression and treatment responses. The current research optimized the cell density to form 3D homotypic and heterotypic tumor spheroids by immobilizing cell suspensions on the lids of standard 10 cm3 Petri dishes. Longitudinal analysis was performed to generate growth curves for homotypic versus heterotypic tumor/fibroblasts spheroids. Finally, the proliferative impact of fibroblasts (COS7 cells) and liver myofibroblasts (LX2) on homotypic tumor (Hep3B) spheroids was investigated. A seeding density of 3,000 cells (in 20 &#181;L media) successfully yielded Huh7/COS7 heterotypic spheroids, which displayed a steady increase in size up to culture day 8, followed by growth retardation. This finding was corroborated using Hep3B homotypic spheroids cultured in LX2 (human hepatic stellate cell line) conditioned medium (CM). LX2 CM triggered the proliferation of Hep3B spheroids compared to control tumor spheroids. In conclusion, this protocol has shown that 3D tumor spheroids can be used as a simple, economical, and prescreen in vitro tool to study tumor-stromal interactions more comprehensively.

Comprehensive in vitro models that faithfully recapitulate the relevant human disease are lacking. The current study presents three-dimensional (3D) tumor spheroid creation and culture, a reliable in vitro tool to study the tumor-stromal interaction in human hepatocellular carcinoma.

The aggressiveness and lack of well-tolerated and widely effective treatments for advanced hepatocellular carcinoma (HCC), the predominant form of liver ...

Modeling Brain Metastasis by Internal Carotid Artery Injection of Cancer Cells

Brain metastasis is a cause of severe morbidity and mortality in cancer patients. Critical aspects of metastatic diseases, such as the complex neural microenvironment and stromal cell interaction, cannot be entirely replicated with in vitro assays; thus, animal models are critical for investigating and understanding the effects of therapeutic intervention. However, most brain tumor xenografting methods do not produce brain metastases consistently in terms of the time frame and tumor burden. Brain metastasis models generated by intracardiac injection of cancer cells can result in unintended extracranial tumor burden and lead to non-brain metastatic morbidity and mortality. Although intracranial injection of cancer cells can limit extracranial tumor formation, it has several caveats, such as the injected cells frequently form a singular tumor mass at the injection site, high leptomeningeal involvement, and damage to brain vasculature during needle penetration. This protocol describes a mouse model of brain metastasis generated by internal carotid artery injection. This method produces intracranial tumors consistently without the involvement of other organs, enabling the evaluation of therapeutic agents for brain metastasis.

Brain metastasis is a cause of severe morbidity and mortality in cancer patients. Most brain metastasis mouse models are complicated by systemic metastases confounding analysis of mortality and therapeutic intervention outcomes. Presented here is a protocol for internal carotid injection of cancer cells that produces consistent intracranial tumors with minimal systemic tumors.

Brain metastasis is a cause of severe morbidity and mortality in cancer patients. Critical aspects of metastatic diseases, such as the complex neural ...

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex vivo drug screenings. However, current adenosine triphosphate (ATP)-based drug screening assays do not capture the complexity of a drug response (cytostatic or cytotoxic) and intratumor heterogeneity that has been shown to be retained in PDTOs due to a bulk readout. Live-cell imaging is a powerful tool to overcome this issue and visualize drug responses more in-depth. However, image analysis software is often not adapted to the three-dimensionality of PDTOs, requires fluorescent viability dyes, or is not compatible with a 384-well microplate format. This paper describes a semi-automated methodology to seed, treat, and image PDTOs in a high-throughput, 384-well format using conventional, widefield, live-cell imaging systems. In addition, we developed viability marker-free image analysis software to quantify growth rate-based drug response metrics that improve reproducibility and correct growth rate variations between different PDTO lines. Using the normalized drug response metric, which scores drug response based on the growth rate normalized to a positive and negative control condition, and a fluorescent cell death dye, cytotoxic and cytostatic drug responses can be easily distinguished, profoundly improving the classification of responders and non-responders. In addition, drug-response heterogeneity can by quantified from single-organoid drug response analysis to identify potential, resistant clones. Ultimately, this method aims to improve the prediction of clinical therapy response by capturing a multiparametric drug response signature, which includes kinetic growth arrest and cell death quantification.

This protocol describes a semi-automated method for medium- to high-throughput organoid drug screenings and microscope-agnostic, automated image analysis software to quantify and visualize multiparametric, single-organoid drug responses to capture intratumor heterogeneity.

Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex ...