This work was supported by NIH grants R01-GM-098977 and R21-AI-098122 to CBK and KEG. RSS was supported in part by a Luce Foundation Fellowship and in part by a Thayer Innovation Program Fellowship from the Thayer School of Engineering.

Four major activities comprise the peptide-MHC II binding assay: 1-Binding: Test peptides compete with labeled control peptides for solution phase binding of soluble MHC II proteins. Binding is measured over a wide range of test peptide concentrations. 2-Capture: After the binding reaction approaches equilibrium, peptide-MHC II complexes are captured and separated from unbound peptide and protein by conformation-dependent recognition with an immobilized antibody. 3-Detection: Captured control peptide is quantitatively detected using time-resolved fluorescence. 4-Analysis: Spectroscopic data are processed, plotted and analyzed to ascertain dose-dependent binding properties of test peptide and MHC II proteins.
At various steps in the procedure below, the use of a liquid handling robot is recommended if one is available. Particularly in a 384-well format, automated dispensing and dilution of liquids minimizes user error, results in more consistent well-to-well volumes, and yields narrower 95% confidence intervals for IC50 values compared to manual 96-well assays (data not shown). If a liquid handling robot is not available, the steps annotated with &#34;(liquid handling robot)&#34; may be done by hand. Similarly, if possible, an automated plate washer that is compatible with the 384-well format is recommended. This effectively standardizes the plate washing process. If a plate washer is not available, the steps annotated with &#34;(plate washer)&#34; may be done by hand.
1. Coat the ELISA Plate (Day 1)
<ol>
	<li>Dilute L243 antibody stock&#160;(0.5 mg/ml) in Borate Buffer to a working concentration of 10 &#181;g/ml.
	<ol>
		<li>To coat a single 384-well plate, add 200 &#181;l of stock antibody to 9.8 ml of Borate Buffer. (See supplemental spreadsheet calculator for alternative scaling).</li>
	</ol>
	</li>
	<li>Add 25 &#181;l of the L243 antibody solution to each well of a 384-well high-binding white ELISA plate.&#160;(liquid handling robot)</li>
	<li>Seal the plate with a polyester film and incubate overnight at 4 &#176;C. 
	Note: Each 384-well ELISA plate can accommodate up to 15 test peptides at eight dilutions for one MHC II allele, as well as the positive and negative controls. This will ultimately yield triplicate measurements.</li>
</ol>
2. Make the Test Peptide Dilutions (Day 1)
<ol>
	<li>Starting with a 10 mM stock of each test peptide in DMSO, make the following dilution series in Citrate Phosphate Buffer using 384-well polypropylene plates (Table 1). (liquid handling robot) 
	Note: A full 384-well polypropylene plate requires three separate ELISA plates. One third of a polypropylene plate will require only one ELISA plate (Figure 1).</li>
</ol>
<table border="0" cellpadding="0" cellspacing="0">
	<tbody align="center">
		<tr>
			<td rowspan="2">Dilution Number</td>
			<td>Volume to take from prior dilution/stock</td>
			<td>Volume Citrate Buffer to add</td>
			<td>Conc. of Dilution</td>
			<td>Conc. in Binding Reaction</td>
			<td>Conc. in Neutralized ELISA</td>
		</tr>
		<tr>
			<td>(&#956;l)</td>
			<td>(&#956;l)</td>
			<td>(&#956;M)</td>
			<td>(&#956;M)</td>
			<td>(&#956;M)</td>
		</tr>
		<tr>
			<td>1</td>
			<td>0.7</td>
			<td>35.1</td>
			<td>195.531</td>
			<td>97.765</td>
			<td>48.883</td>
		</tr>
		<tr>
			<td>2</td>
			<td>14.3</td>
			<td>14.3</td>
			<td>97.765</td>
			<td>48.883</td>
			<td>24.441</td>
		</tr>
		<tr>
			<td>3</td>
			<td>7.1</td>
			<td>28.7</td>
			<td>19.389</td>
			<td>9.695</td>
			<td>4.847</td>
		</tr>
		<tr>
			<td>4</td>
			<td>14.3</td>
			<td>14.3</td>
			<td>9.695</td>
			<td>4.847</td>
			<td>2.424</td>
		</tr>
		<tr>
			<td>5</td>
			<td>7.1</td>
			<td>28.7</td>
			<td>1.923</td>
			<td>0.961</td>
			<td>0.481</td>
		</tr>
		<tr>
			<td>6</td>
			<td>14.3</td>
			<td>14.3</td>
			<td>0.961</td>
			<td>0.481</td>
			<td>0.24</td>
		</tr>
		<tr>
			<td>7</td>
			<td>7.1</td>
			<td>28.7</td>
			<td>0.191</td>
			<td>0.095</td>
			<td>0.048</td>
		</tr>
		<tr>
			<td>8</td>
			<td>14.3</td>
			<td>14.3</td>
			<td>0.095</td>
			<td>0.048</td>
			<td>0.024</td>
		</tr>
		<tr>
			<td colspan="6">Remove and discard 7.1 &#956;l of peptide solution from dilution number 8.</td>
		</tr>
	</tbody>
</table>
Table 1. Preparation of test peptide dilution series.
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/51308/51308fig1highres.jpg" src="/files/ftp_upload/51308/51308fig1.jpg" /> 
Figure 1. Plate Map of Peptide Binding Assay. (A) Map of the peptide dilutions in polypropylene 384-well plates. Each polypropylene plate can accommodate three groups of 15 peptides in competition binding reactions against a single MHC II allele. (B) After the binding reaction approaches equilibrium, each peptide group is transferred, in triplicate, to a separate 384-well ELISA plate that has been precoated with anti-MHC II antibody.
3. Prepare the MHC II Master Mix (Day 1)
<ol>
	<li>Make the Reaction Buffer
	<ol>
		<li>Add 80 &#181;l of 1 mM Pefa Bloc and 60 &#181;g of octyl-&#946;-D-glucopyranaside to 3,920 &#181;l of Citrate Phosphate Buffer. (See supplemental spreadsheet calculator for alternative scaling).</li>
	</ol>
	</li>
	<li>Dilute selected MHC II stock solutions to 101 nM in Reaction Buffer using a 15 ml conical tube (Table 2). 
	Note: MHC II concentration will ultimately be 50 nM in the binding reaction and 25 nM in the neutralized ELISA assay. The MHC II stock concentrations will vary depending on manufacturer lot number. (See supplemental spreadsheet calculator).</li>
</ol>
<table border="0" cellpadding="0" cellspacing="0" width="376">
	<tbody>
		<tr>
			<td>MHC II Allele DRB1*:</td>
			<td>1501</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (mg/ml)</td>
			<td>1.3</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (&#181;M)</td>
			<td>20</td>
		</tr>
		<tr>
			<td>Vol. MHC II Stock to Add (&#181;l)</td>
			<td>14.65</td>
		</tr>
		<tr>
			<td>Vol. Reaction Buffer to Add (&#181;l):</td>
			<td>2885.35</td>
		</tr>
		<tr>
			<td>MHC II Master Mix Concentration (nM)</td>
			<td>101</td>
		</tr>
	</tbody>
</table>
Table 2. Preparation of MHC II master mix.
4. Prepare the Negative and Positive Controls (Day 1)
<ol>
	<li>Add 21.5 &#181;l of Citrate Phosphate Buffer to the &#34;negative control&#34; well of the 384-well polypropylene plate from step 2.1 (Figure 1A).</li>
	<li>Add 21.5 &#181;l of Citrate Phosphate Buffer to the &#34;positive control&#34; well of the 384-well polypropylene plate. 
	Note: the &#34;positive control&#34; will be completed in section 5, below.</li>
	<li>Remove 49.5 &#181;l of the MHC II Master Mix from step 3.2 and pipette into a 1.5 ml Eppendorf tube.</li>
	<li>Add 0.5 &#181;l of Citrate Buffer to the 49.5 &#181;l of the MHC II Master Mix from step 4.3.</li>
	<li>Add 21.5 &#181;l of the concentration adjusted MHC II Master Mix from step 4.4 to the &#34;negative control&#34; well of the 384-well polypropylene plate (Figure 1A). 
	Note: this sample represents the zero-signal negative control containing MHC II protein, NO control peptide, and NO test peptide.</li>
</ol>
5. Add the Appropriate Control Peptide to the MHC II Master Mix (Day 1)
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>MHC II Allele DRB1*</td>
			<td>Biotinylated Control Peptide</td>
			<td>(residues)</td>
			<td>Sequence</td>
		</tr>
		<tr>
			<td>0101</td>
			<td>Flu-HA-B</td>
			<td>(306-318)</td>
			<td>Biotin-(Ahx)(Ahx)PRYVKQNTLKLAT-amide</td>
		</tr>
		<tr>
			<td>0301</td>
			<td>Myoglobin</td>
			<td>(137-148)</td>
			<td>Biotin-(Ahx)-(Ahx)-LFRKDIAAKYKE-OH</td>
		</tr>
		<tr>
			<td>0401</td>
			<td>YAR-B</td>
			<td>(1-14)</td>
			<td>Biotin-(Ahx)(Ahx)YARFQSQTTLKQKT-OH</td>
		</tr>
		<tr>
			<td>0701</td>
			<td>TetTox-B</td>
			<td>(830-843)</td>
			<td>Biotin-(Ahx)(Ahx)QYIKANSKFIGITE-OH</td>
		</tr>
		<tr>
			<td>1101</td>
			<td>Flu-HA-B</td>
			<td>(306-318)</td>
			<td>Biotin-(Ahx)(Ahx)PRYVKQNTLKLAT-amide</td>
		</tr>
		<tr>
			<td>1501</td>
			<td>MBP-B&#160;</td>
			<td>(84-102)</td>
			<td>Biotin-(Ahx)(Ahx)NPVVHFFKNIVTPRTPPPS-OH</td>
		</tr>
	</tbody>
</table>
*We recommend ordering 10 mg scale for economy. 
Table 3. Control Peptides for Various MHC II Alleles.*
<ol>
	<li>Dilute the Control peptide (stored at 400 &#181;M in DMSO) 1:20 in fresh DMSO to yield a diluted stock at 20 &#181;M.
	<ol>
		<li>Add 25 &#181;l 400 &#181;M control peptide to 475 &#181;l DMSO. Note: this is a large excess, but allows accurate handling of viscous DMSO solutions.</li>
	</ol>
	</li>
	<li>Further dilute the Control Peptide from step 5.1 (20 &#181;M) 1:100 into the MHC II Master Mix from step 3.2.
	<ol>
		<li>Add 28.8 &#181;l of Control Peptide diluted in step 5.2 to the remaining 2,850.5 &#181;l of the MHC II Master Mix.</li>
	</ol>
	</li>
	<li>Add 21.5 &#181;l of the MHC II Master Mix with control peptide (step 5.2) to the positive control well of the 384-well polypropylene plate (step 4.2) (Figure 1A). Note: this sample represents the high-signal positive control containing MHC II protein, control peptide, and NO test peptide.</li>
</ol>
6. Make the Binding Reaction (Day 1)
<ol>
	<li>Add the MHC II Master Mix containing Control Peptide (step 5.2) to each of the Test Peptide dilutions (step 2.1) at a 1:1 ratio to create the Binding Reaction.
	<ol>
		<li>Add 21.5 &#181;l of MHC II Master Mix containing Control Peptide from step 5.2 to 21.5 &#181;l of each Test Peptide dilution from step 2.1. (liquid handling robot)</li>
	</ol>
	</li>
	<li>Seal the Binding Reaction with a polyester film and incubate 12-24 hr in a non-CO2 controlled incubator at 37 &#176;C without shaking.</li>
</ol>
7. Neutralize and Transfer the Binding Reaction (Day 2)
<ol>
	<li>Remove the 384-well polypropylene plate containing the Binding Reaction (step 6.2) from the 37 &#176;C incubator.</li>
	<li>Dilute the Binding Reaction 1:1 with Neutralization Buffer. (liquid handling robot)
	<ol>
		<li>Add 43 &#181;l of the Neutralization Buffer to each Binding Reaction well.</li>
	</ol>
	</li>
	<li>Remove the ELISA plate (step 1.3) from 4 &#176;C and wash 3x with 60 ml/well of PBS-0.05% Tween 20. (plate washer)</li>
	<li>Transfer 25 &#956;l of each neutralized Binding Reaction from step 7.2 into triplicate wells of the antibody-coated 384-well ELISA plate from step 7.3. (Figure 1) (liquid handling robot)</li>
	<li>Cover the ELISA plate with a polyester film and place in a 37 &#176;C incubator for 2.5 hr or in a 4 &#176;C refrigerator overnight.</li>
</ol>
8. ELISA Development (Day 2 or 3)
<ol>
	<li>Remove the ELISA plate from step 7.5, wash 3 times with 60 &#181;l/well of PBS-0.05% Tween. (plate washer)</li>
	<li>Dilute Streptavidin-Europium stock solution (0.1 mg/ml streptavidin, 7 Eu3+/streptavidin) 1,000-fold in the DELFIA Assay Buffer.
	<ol>
		<li>For one 384-well plate, dilute 10 &#181;l Streptavidin-Europium in 10 ml Assay Buffer.</li>
	</ol>
	</li>
	<li>Add 25 &#181;l of the diluted Streptavidin-Europium to each well of the 384-well ELISA plate from step 8.1. (liquid handling robot)</li>
	<li>Cover the plate with a polyester film and place in the dark at room temperature for 1 hr. Note: During the 1 hr incubation, remove the Enhancement Solution from the refrigerator and set aside 10 ml/plate in the dark at room temperature.</li>
	<li>Following the 1-hr incubation, wash the 384-well ELISA plate from step 8.3 3x with 60 &#181;l/well of PBS-0.05% Tween. (plate washer)</li>
	<li>Add 25 &#181;l of Enhancement Solution to each well of the 384-well ELISA plate from step 8.5. (liquid handling robot)</li>
	<li>Cover the 384-well ELISA plate with a polyester film and allow the plate to sit in the dark at room temperature for 10-15 min.</li>
	<li>Following the 10-15 min incubation, read the fluorescence of the 384-well ELISA plate from step 8.7 using a time resolved fluorescent plate reader with Europium settings (for example, Start Int: 200, Stop Int: 1,000, Ex. 340, Em. 615, Cutoff: None, PMT: Auto, Reads/Well: 50).</li>
</ol>
9. Data Analysis
<ol>
	<li>Enter data in XY format graph with 3 replicate values in side-by-side columns (X=test peptide concentration; Y=fluorescence measurement).</li>
	<li>Log transform the X values for all data: X=log(X)</li>
	<li>Fit the log transformed data with the one-site competitive binding model to extract the IC50 value.</li>
	<li>Constrain the bottom value to the average negative control value.</li>
	<li>Globally fit the top value and insure that the global fit parameter is below or equal to the positive control.</li>
</ol>

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E., Bailey-Kellogg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure-guided+deimmunization+of+therapeutic+proteins.">Structure-guided deimmunization of therapeutic proteins.</a> J. Comput. Biol. 20, 152-165 (2013).</li><li>Parker, A. S., Griswold, K. E., Bailey-Kellogg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+therapeutic+proteins+to+delete+T-cell+epitopes+while+maintaining+beneficial+residue+interactions.">Optimization of therapeutic proteins to delete T-cell epitopes while maintaining beneficial residue interactions.</a> J. Bioinform. Comput. Biol. 9, 207-229 (2011).</li><li>Parker, A. S., Zheng, W., Griswold, K. E., Bailey-Kellogg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+algorithms+for+functional+deimmunization+of+therapeutic+proteins.">Optimization algorithms for functional deimmunization of therapeutic proteins.</a> BMC Bioinformatics. 11, 180 (2010).</li><li>De Groot, A. S., Martin, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reducing+risk,+improving+outcomes:+Bioengineering+less+immunogenic+protein+therapeutics.">Reducing risk, improving outcomes: Bioengineering less immunogenic protein therapeutics.</a> Clin. Immunol. 131, 189-201 (2009).</li><li>Wang, P., 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=A+systematic+assessment+of+MHC+class+II+peptide+binding+predictions+and+evaluation+of+a+consensus+approach.">A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach.</a> PLoS Comput. Biol. 4, e1000048 (2008).</li><li>Sette, A., Rappuoli, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reverse+vaccinology:+developing+vaccines+in+the+era+of+genomics.">Reverse vaccinology: developing vaccines in the era of genomics.</a> Immunity. 33, 530-541 (2010).</li><li>Moise, L., Cousens, L., Fueyo, J., De Groot, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Harnessing+the+power+of+genomics+and+immunoinformatics+to+produce+improved+vaccines.">Harnessing the power of genomics and immunoinformatics to produce improved vaccines.</a> Expert Opin. Drug Discov. 6, 9-15 (2011).</li><li>Sturniolo, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generation+of+tissue-specific+and+promiscuous+HLA+ligand+databases+using+DNA+microarrays+and+virtual+HLA+class+II+matrices.">Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices.</a> Nat. Biotechnol. 17, 555-561 (1999).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biologic+drugs+set+to+top+2012+sales.">Biologic drugs set to top 2012 sales.</a> Nat. Med. 18, 636 (2012).</li><li>Seib, K. L., Zhao, X., Rappuoli, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Developing+vaccines+in+the+era+of+genomics:+a+decade+of+reverse+vaccinology.">Developing vaccines in the era of genomics: a decade of reverse vaccinology.</a> Clin. Microbiol. Infect. 18, 109-116 (2012).</li><li>Jiang, W., Boder, E. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+engineering+and+analysis+of+peptide+binding+to+class+II+MHC.">High-throughput engineering and analysis of peptide binding to class II MHC.</a> Proc. Natl. Acad. Sci. U.S.A. 107, 13258-13263 (2010).</li><li>Justesen, S., Harndahl, M., Lamberth, K., Nielsen, L. -. L. B., Buus, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+recombinant+MHC+class+II+molecules+and+high-throughput+peptide-binding+assays.">Functional recombinant MHC class II molecules and high-throughput peptide-binding assays.</a> Immun. 5, 1-20 (2009).</li></ol>

Leonard Moise is employed by and holds stock options in EpiVax, Inc., a privately owned biotechnology company located in Providence, RI. The work contained in this research report is free of any bias that might be associated with the commercial goals of the company.

Biotherapeutics have established themselves as a cornerstone of modern medicine, representing four of the top five selling drugs in 201232. The biopharmaceutics sector has shown sustained growth for several years6, and the ongoing development of novel agents as well as the emergence of biosimilars has expanded biopharmaceutical pipelines. Looking to the future, assessing and mitigating the immunogenicity of protein therapeutics will become an integral part of early stage biotherapeutic development. ...

An erratum was issued for: <a href="https://www.jove.com/video/51308/a-high-throughput-mhc-ii-binding-assay-for-quantitative-analysis">A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes</a>.&#160; There were typos in the Protocol section and Table 2.
Step 3.1.1 in the Protocol was updated from:
Add 80 &#181;l of 1 mM Pefa Bloc and 60 mg of octyl-&#946;-D-glucopyranaside to 3,920 &#181;l of Citrate Phosphate Buffer. (See supplemental spreadsheet calculator for alternative scaling).
to:
Add 80 &#181;l of 1 mM Pefa Bloc and 60 &#181;g of octyl-&#946;-D-glucopyranaside to 3,920 &#181;l of Citrate Phosphate Buffer. (See supplemental spreadsheet calculator for alternative scaling).
Step 5.2&#160;in the Protocol was updated from:
Further dilute the Control Peptide from step 5.1 (20 mM) 1:100 into the MHC II Master Mix from step 3.2.
to:
Further dilute the Control Peptide from step 5.1 (20 &#181;M) 1:100 into the MHC II Master Mix from step 3.2.
Table 2 was updated from:
<table border="0" cellpadding="0" cellspacing="0" width="376">
	<tbody>
		<tr>
			<td>MHC II Allele DRB1*:</td>
			<td>1501</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (mg/ml)</td>
			<td>1.3</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (mM)</td>
			<td>20</td>
		</tr>
		<tr>
			<td>Vol. MHC II Stock to Add (ml)</td>
			<td>14.65</td>
		</tr>
		<tr>
			<td>Vol. Reaction Buffer to Add (ml):</td>
			<td>2885.35</td>
		</tr>
		<tr>
			<td>MHC II Master Mix Concentration (nM)</td>
			<td>101</td>
		</tr>
	</tbody>
</table>
to:
<table border="0" cellpadding="0" cellspacing="0" width="376">
	<tbody>
		<tr>
			<td>MHC II Allele DRB1*:</td>
			<td>1501</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (mg/ml)</td>
			<td>1.3</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (&#956;M)</td>
			<td>20</td>
		</tr>
		<tr>
			<td>Vol. MHC II Stock to Add (&#956;l)</td>
			<td>14.65</td>
		</tr>
		<tr>
			<td>Vol. Reaction Buffer to Add (&#956;l):</td>
			<td>2885.35</td>
		</tr>
		<tr>
			<td>MHC II Master Mix Concentration (nM)</td>
			<td>101</td>
		</tr>
	</tbody>
</table>

An erratum was issued for: <a href="https://www.jove.com/video/51308/a-high-throughput-mhc-ii-binding-assay-for-quantitative-analysis">A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes</a>.&#160; There were typos in the Protocol section and Table 2.

Erratum: A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Proteins are the fastest growing class of therapeutic agents6, and the rapid expansion of biotherapeutic pipelines has focused increasing attention on the challenges associated with development and use of protein drugs. One unique consideration stems from the fact that, in a healthy and functioning immune system, all extracellular proteins are sampled by antigen presenting cells (APCs). Once internalized by APCs, a protein is cleaved into small peptide fragments, and putative immunogenic segments are loaded into the groove of class II major histocompatibility complex proteins (MHC II). The peptide-MHC II complexes are then displayed on the APC surface, and true immunogenic peptides, termed T cell epitopes, form ternary MHC II-peptide-T cell receptor complexes with cognate CD4 T cell surface receptors7. This critical molecular recognition event initiates a complex signaling cascade, which results in T cell activation, the release of cytokines, CD4 T cell-mediated B cell maturation, and ultimately production of circulating IgG antibodies that bind to and clear the offending exogenous protein. Thus, immunogenic proteins might be deimmunized by identifying constituent T cell epitopes and mutating key residues responsible for MHC II complex formation. It bears noting, however, that T cell epitopes can be numerous and broadly distributed throughout immunogenic proteins, and the majority of epitope-deleting mutations are likely to cause an inadvertent loss of protein function or stability. Therefore, engineering deimmunized biotherapies can be a complex and technically challenging objective, but there exist several examples of successful T cell epitope deletion projects3,5,8-12. Unlike grafting-based &#34;humanization&#34;, which is largely restricted to antibody therapeutics, epitope deletion can be applied to essentially any protein target regardless of sequence, structure, function, or the availability of homologous human scaffolds. The first step to implementing such an approach is identification of key peptide epitopes embedded within the target protein sequence.
High throughput biochemical assays using synthetic peptides and recombinant human MHC II molecules can provide rapid preliminary insights into epitope identification and mitigation1,3-5. These ELISA type assays can be a powerful complement to other protein/vaccine design and development tools. For example, one well established experimental approach to epitope mapping relies on time, labor, and resource intensive ex vivo cell proliferation assays 15. Briefly, the primary sequence of a target protein is first divided into a panel of overlapping peptides, often 15-mers with 12 residues overlap between adjacent peptides. The peptide panel is chemically synthesized and the immunogenicity of each peptide is tested in one of several different immunoassays that employ peripheral blood mononuclear cells (PBMC) isolated from human donors13,14. To provide greater confidence in results, peptides are typically tested in replicate with PBMC from 50 or more different donors. In cases where deimmunization is the ultimate objective, the work is compounded further by the need to produce additional panels of mutated peptides and test the new peptide panels in PBMC assays before introducing any deimmunizing mutations into the full length protein for subsequent functional analysis10. While these cellular assays remain the gold standard for assessing immunogenic potential in human patients, the efficiency of such an exhaustive approach might be improved by prefiltering putative immunogenic epitopes using a rapid and high throughput MHC II-peptide binding assay.
Likewise, biochemical peptide-MHC II binding assays can be combined with predictive in silico methods to radically accelerate the epitope identification process. There exist a variety of computational tools for T cell epitope prediction; examples include ProPred16, MHCPred17, SVRMHC18, ARB19, SMM-align20, NetMHCIIpan21 as well as proprietary tools such as EpiMatrix by EpiVax22. Likewise, epitope predictors have recently been combined with other bioinformatics and molecular modeling tools to yield integrated protein deimmunization algorithms designed to mitigate the risk that deimmunizing mutations might disrupt protein structure and function23-26. While several epitope predictors have proven to be reasonably accurate27,28, computational results invariably require experimental validation. Rapid, high throughput, and cost effective experimental methods are best suited as a preliminary filter for in silico epitope predictions.
In a similar vein, epitope predictors can drive antigen selection for reverse vaccinology29,30. For example, advances in bioinformatics have yielded whole genome screens that rapidly identify vaccine candidates in the form of whole proteins or peptide epitopes extracted from pathogen proteomes. While this enabling technology is reshaping discovery and development of protective vaccines, it introduces a new challenge in the form of intractably large lists of immunogenic vaccine candidates. High throughput peptide-MHC II binding assays can guide epitope selection by quantifying peptide binding affinity and binding promiscuity among multiple MHC II alleles. As with protein deimmunization, such experimental methods are ultimately required to validate computational prediction of promising vaccine leads.
Here, a peptide-MHC II binding assay scaled to 384-well format is described. The protocol is highly parallelized and reduces reagent costs by 75% compared to previously described 96-well plate formats1,3-5. Using a single liquid handling robot, this method allows one researcher to easily analyze approximately ninety test peptides in triplicate over a range of eight concentrations and four MHC II allele types in less than 48 hr. This article describes the setup of one 384-well ELISA plate for analysis of seven experimental peptides against one MHC II allele, but spread sheet calculators are provided as supplemental material so as to easily scale the experiment to any number of desired peptides and/or MHC II molecules.

<table border="0" cellpadding="0" cellspacing="0">
	<colgroup>
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="35">
			<td height="35" style="height:35px;width:587px;">Sodium Tetraborate Decahydrate</td>
			<td style="width:357px;">Sigma</td>
			<td style="width:217px;">221732</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Citric Acid</td>
			<td>Sigma</td>
			<td>C1901</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Dibasic Sodium Phosphate</td>
			<td>Sigma</td>
			<td>S7907</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Trizma HCl</td>
			<td>OmniPur</td>
			<td>9310</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Tween-20</td>
			<td>Sigma</td>
			<td>P7949</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">100% DMSO</td>
			<td>Sigma</td>
			<td>D8418</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Octyl-&#946;-D-Glucopyranaside</td>
			<td>Fischer</td>
			<td>29836-26-8</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Pefa Bloc SCF</td>
			<td>Roche</td>
			<td>1158591600</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Dulbecco&#8217;s 10X Phosphate Buffered Saline</td>
			<td>Invitrogen</td>
			<td>14200-166</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">DELFIA Assay Buffer</td>
			<td>Perkin Elmer</td>
			<td>4002-0010</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">DELFIA Enhancement Buffer</td>
			<td>Perkin Elmer</td>
			<td>4001-0010</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Europium Labelled Streptavidin</td>
			<td>Perkin Elmer</td>
			<td>1244-360</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">L243 anti-HLA-DR antibody</td>
			<td>Biolegend</td>
			<td>307602</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Biotinylated tracer peptides</td>
			<td>21st Century Biochemicals</td>
			<td>Custom Order</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Test peptides (1-4mg, 85% purity)</td>
			<td>Genscript</td>
			<td>Custom Order</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Purified HLA-DRB1 monomers (non-biotinylated)</td>
			<td>Benaroya Research Institute*</td>
			<td>Custom Order</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">384-well white EIA/RIA plate</td>
			<td>Thermo</td>
			<td>460372</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Polypropylene 384-well plate</td>
			<td>Costar</td>
			<td>3656</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Film, AxySeal, 80 &#181;m, ELISA Applicator&#160;</td>
			<td>Axygen Asicentific</td>
			<td>PCR-SP</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">MilliQ Water</td>
			<td>N/A</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Epimotion Liquid Handler (or similar)</td>
			<td>Eppendorf</td>
			<td>5075</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Select TS Plate Washer (or similar)</td>
			<td>BioTek</td>
			<td>405</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">SpectraMax Gemini Microplate Reader (or similar)</td>
			<td>Molecular Devices</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4" height="98" style="height:99px;width:1161px;">*Recombinant human MHC II molecules can be obtained from the Benaroya Tetramer Core Laboratory. See: https://www.benaroyaresearch.org/our-research/core-resources/tetramer-core-laboratory</td>
		</tr>
	</tbody>
</table>

a high throughput mhc ii binding assay for quantitative analysis of peptide epitopes

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design1,2. Here, a peptide-MHC II binding assay is scaled to 384-well format. The scaled down protocol reduces reagent costs by 75% and is higher throughput than previously described 96-well protocols1,3-5. Specifically, the experimental design permits robust and reproducible analysis of up to 15 peptides against one MHC II allele per 384-well ELISA plate. Using a single liquid handling robot, this method allows one researcher to analyze approximately ninety test peptides in triplicate over a range of eight concentrations and four MHC II allele types in less than 48 hr. Others working in the fields of protein deimmunization or vaccine design and development may find the protocol to be useful in facilitating their own work. In particular, the step-by-step instructions and the visual format of JoVE should allow other users to quickly and easily establish this methodology in their own labs.

The mature peptide sequence of Enterobacter cloacae P99 beta-lactamase (BLA) (Genbank ID# X07274.1) was analyzed with ProPred16 for putative peptide binders to MHC II allele DRB1*1501 (Table 4). ProPred identified 117 nonamer peptides with an obligatory P1 anchor residue (i.e. an M, L, I, V, F, Y, or W at position 1, which is required for MHC II binding31). At a 5% threshold, only peptides with scores greater than or equal to 2.6 are likely binders. Thus, at the 5%...

Watch this Scientific Journal Video about A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes at JoVE.com

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design. &#160;Here, a peptide-MHC II binding assay scaled to 384-well plates is described. This cost effective format should prove useful in the fields of protein deimmunization and vaccine design and development.

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or ...

a-high-throughput-mhc-ii-binding-assay-for-quantitative-analysis

Research

JoVE Journal

Biology

14.8K ビュー.  Dartmouth College. 組換えヒトMHC II分子と生化学的アッセイは、免疫​​原性エピトープ同定、削除、または設計に迅速かつ定量的な洞察を提供することができます。ここで、384ウェルプレートにスケーリングペプチド-MHC II結合アッセイが記載されている。この費用対効果の高い形式は、タンパク質の脱免疫やワクチンの設計と開発の分野で有用であることを証明する必要?...

ビデオ: A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers

Major histocompatibility complex (MHC) class II tetramers allow the direct visualization of antigen specific CD4+ T cells by flow cytometry.  This method relies on the highly specific interaction between peptide loaded MHC and the corresponding T-cell receptor.  While the affinity of a single MHC/peptide molecule is low, cross-linking MHC/peptide complexes with streptavidin increases the avidity of the interaction, enabling their use as staining reagents.  Because of the relatively low frequencies of CD4+ T cells (~1 in 300,000 for a single specificity) this assay utilizes an in vitro amplification step to increase its threshold of detection.  Mononuclear cells are purified from peripheral blood by Ficoll underlay.  CD4+ cells are then separated by negative selection using biotinylated antibody cocktail and anti-biotin labeled magnetic beads.  Using adherent cells from the CD4- cell fraction as antigen presenting cells, CD4+ T cells are expanded in media by adding an antigenic peptide and IL-2.  The expanded cells are stained with the corresponding class II tetramer by incubating at 37 C for one hour and subsequently stained using surface antibodies such as anti-CD4, anti-CD3, and anti-CD25.  After labeling, the cells can be directly analyzed by flow cytometry.  The tetramer positive cells typically form a distinct population among the expanded CD4+ cells.  Tetramer positive cells are usually CD25+ and often CD4 high.  Because the level of background tetramer staining can vary, positive staining results should always be compared to the staining of the same cells with an irrelevant tetramer.  Multiple variations of this basic assay are possible.  Tetramer positive cells may be sorted for further phenotypic analysis, inclusion in ELISPOT or proliferation assays, or other secondary assays.  Several groups have also demonstrated co-staining using tetramers and either anti-cytokine or anti-FoxP3 antibodies.  

This procedure demonstrates the purification and in vitro expansion of antigen specific CD4+ T cells from whole peripheral blood and their visualization using MHC class II tetramers. Tetramers permit the direct visualization of T cells with a single antigen specificity and defined MHC class II restriction.

Major histocompatibility complex (MHC) class II tetramers allow the direct visualization of antigen specific CD4+ T cells by flow cytometry.  This method ...

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

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 ...

High-throughput Saccharification Assay for Lignocellulosic Materials

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing a biorefinery. These raw materials would constitute a new industrial platform, which is both sustainable and carbon neutral, to replace 
the current dependency on fossil fuel. The recalcitrance to deconstruction observed in lignocellulosic materials is produced by several intrinsic properties 
of plant cell walls. Crystalline cellulose is embedded in matrix polysaccharides such as xylans and arabinoxylans, and the whole structure is encased by the 
phenolic polymer lignin, that is also difficult to digest 1. In order to improve the digestibility of plant materials we need to discover the main bottlenecks 
for the saccharification of cell walls and also screen mutant and breeding populations to evaluate the variability in saccharification 2. These tasks require 
a high throughput approach and here we present an analytical platform that can perform saccharification analysis in a 96-well plate format. This platform has 
been developed to allow the screening of lignocellulose digestibility of large populations from varied plant species. We have scaled down the reaction volumes 
for gentle pretreatment, partial enzymatic hydrolysis and sugar determination, to allow large numbers to be assessed rapidly in an automated system.
This automated platform works with milligram amounts of biomass, performing ball milling under controlled conditions to reduce the plant 
materials to a standardised particle size in a reproducible manner. Once the samples are ground, the automated formatting robot dispenses specified and recorded 
amounts of material into the corresponding wells of 96 deep well plate (Figure 1). Normally, we dispense the same material into 4 wells to have 4 replicates for 
analysis. Once the plates are filled with the plant material in the desired layout, they are manually moved to a liquid handling station (Figure 2). In this 
station the samples are subjected to a mild pretreatment with either dilute acid or alkaline and incubated at temperatures of up to 90&deg;C. The pretreatment solution 
is subsequently removed and the samples are rinsed with buffer to return them to a suitable pH for hydrolysis. The samples are then incubated with an enzyme
mixture for a variable length of time at 50&deg;C. An aliquot is taken from the hydrolyzate and the reducing sugars are automatically determined by the MBTH 
colorimetric method.

A simple, rapid method for determining the saccharification potential of large numbers of plant biomass samples is described. The automated platform for this analysis involves the preparation of the plant biomass for analysis in 96 well plates and the subsequent performance of pretreatment, hydrolysis and quantification of the sugars released.

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing ...

A Quantitative Fitness Analysis Workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.
QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1.
Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.
We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.

Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. ...

High-throughput Functional Screening using a Homemade Dual-glow Luciferase Assay

We present a rapid and inexpensive high-throughput screening protocol to identify transcriptional regulators of alpha-synuclein, a gene associated with Parkinson&#39;s disease. 293T cells are transiently transfected with plasmids from an arrayed ORF expression library, together with luciferase reporter plasmids, in a one-gene-per-well microplate format. Firefly luciferase activity is assayed after 48 hr to determine the effects of each library gene upon alpha-synuclein transcription, normalized to expression from an internal control construct (a hCMV promoter directing Renilla luciferase). This protocol is facilitated by a bench-top robot enclosed in a biosafety cabinet, which performs aseptic liquid handling in 96-well format. Our automated transfection protocol is readily adaptable to high-throughput lentiviral library production or other functional screening protocols requiring triple-transfections of large numbers of unique library plasmids in conjunction with a common set of helper plasmids. We also present an inexpensive and validated alternative to commercially-available, dual luciferase reagents which employs PTC124, EDTA, and pyrophosphate to suppress firefly luciferase activity prior to measurement of Renilla luciferase. Using these methods, we screened 7,670 human genes and identified 68 regulators of alpha-synuclein. This protocol is easily modifiable to target other genes of interest.

We present a rapid and inexpensive screening method for identifying transcriptional regulators using high-throughput robotic transfections and a homemade dual-glow luciferase assay. This protocol rapidly generates direct side-by-side functional data for thousands of genes and is easily modifiable to target any gene of interest.

We present a rapid and inexpensive high-throughput screening protocol to identify transcriptional regulators of alpha-synuclein, a gene associated with ...

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.
BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC ...

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic chromosome, synIII1, and one synthetic chromosome arm, synIXR2, have been constructed and their in vivo function validated in the absence of the corresponding wild type chromosomes. An important design feature of Sc2.0 chromosomes is the introduction of PCRTags, which are short, re-coded sequences within open reading frames (ORFs) that enable differentiation of synthetic chromosomes from their wild type counterparts. PCRTag primers anneal selectively to either synthetic or wild type chromosomes and the presence/absence of each type of DNA can be tested using a simple PCR assay. The standard readout of the PCRTag assay is to assess presence/absence of amplicons by agarose gel electrophoresis. However, with an average PCRTag amplicon density of one per 1.5 kb and a genome size of ~12 Mb, the completed Sc2.0 genome will encode roughly 8,000 PCRTags. To improve throughput, we have developed a real time PCR-based detection assay for PCRTag genotyping that we call qPCRTag analysis. The workflow specifies 500 nl reactions in a 1,536 multiwell plate, allowing us to test up to 768 PCRTags with both synthetic and wild type primer pairs in a single experiment.

Designer chromosomes of the Synthetic Yeast Genome project, Sc2.0, can be distinguished from their native counterparts using a PCR-based genotyping assay called PCRTagging, which has a presence/absence endpoint. Here we describe a high-throughput real time PCR detection method for PCRTag genotyping.

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic ...

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of energy homeostasis and obesity. The small size of zebrafish allows for assays on embryos to be conducted in a 96- or 384-well plate format, Morpholino and CRISPR based technologies promote ease of genetic manipulation, and drug treatment by bath application is viable. Moreover, zebrafish are ideal for forward genetic screens allowing for novel gene discovery. Given the relative novelty of zebrafish as a model for obesity, it is necessary to develop tools that fully exploit these benefits. Herein, we describe a method to measure energy expenditure in thousands of embryonic zebrafish simultaneously. We have developed a whole animal microplate platform in which we use 96-well plates to isolate individual fish and we assess cumulative NADH2 production using the commercially available cell culture viability reagent alamarBlue. In poikilotherms the relationship between NADH2 production and energy expenditure is tightly linked. This energy expenditure assay creates the potential to rapidly screen pharmacological or genetic manipulations that directly alter energy expenditure or alter the response to an applied drug (e.g. insulin sensitizers).

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism for the study of energy homeostasis. By utilizing a NADH2 sensitive redox indicator, alamar Blue, we have developed an assay that measures the metabolic rate of zebrafish larvae in a 96 well plate format and can be applied to drug or gene discovery.

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of ...