Name: rapid determination of antibody-antigen affinity by mass photometry
Uploaded: 2021-02-08T13:00:00.000+00:00
Duration: 10 min 17 s
Description: Watch this Scientific Journal Video about Rapid Determination of Antibody-Antigen Affinity by Mass Photometry at JoVE.com

We thank Keir Neuman for his critical reading of the manuscript. This work was supported by the intramural program of the NHLBI, NIH.

1. Prepare the flow chambers
<ol>
	<li>Clean the glass coverslips
	<ol>
		<li>Using wash bottles with distilled water, ethanol, and isopropanol, rinse the 24 mm&#160;x&#160;50&#160;mm coverslips in the following order: water, ethanol, water, isopropanol, water. Dry the coverslips with a stream of clean nitrogen. It is important to rinse the coverslips from top to bottom, holding the bottom corner with soft-tipped forceps. Dry the coverslip in the same direction to avoid transferring contamination from the forceps (Figure 2A).</li>
		<li>Similarly, rinse the 24 mm x 24 mm coverslips with distilled water, ethanol, and distilled water. Dry the coverslips with a stream of clean nitrogen.</li>
		<li>Identify the working side of the coverslip, place a drop of distilled water on the surface of the clean coverslip and follow steps 3.1&#8211;3.2 of the protocol. Usually only one side of the 24 mm x 50 mm coverslip has the optical quality suitable for MP measurements. 
		NOTE: After focusing, no significant surface imperfections should be detectable, and the &#8220;signal&#8221; value shown in the data collection software should be less than 0.05% (Figure 1A-C). The working sides of all coverslips in the box are oriented in the same direction. The same procedure should be used to test the efficiency of the coverslip cleaning.</li>
	</ol>
	</li>
	<li>Assemble the flow chamber
	<ol>
		<li>Position the 24 mm x 24 mm coverslip on a piece of aluminum foil. Place strips of double-sided tape on top of the 24 mm x 24 mm coverslip as shown in Figure 2B and cut the tape along the edge of the glass. Separate the coverslip from the aluminum foil and attach it to the working side of the 24 mm x 50 mm coverslip (Figure 2C). 
		NOTE: Channel size can vary, but a width of 3 mm&#8211;5 mm is recommended. Wider channels require larger sample volumes and very narrow channels may be difficult to load. Usually, two parallel channels can easily be created on the 24 mm x 24 mm coverslip. Protocol can be paused here.</li>
	</ol>
	</li>
</ol>
2. Prepare the antibody-antigen samples for the affinity measurements
<ol>
	<li>Filter at least 2 mL of the PBS buffer using 0.22 &#181;m syringe filters to remove dust particles or aggregates. Centrifuge the protein stock for 10 minutes at the maximum speed of the tabletop centrifuge (approximately 16,000 x g). 
	NOTE: PBS is the recommended buffer for this protocol, but MP has no particular buffer requirements, and other biological buffers are also acceptable. However, high glycerol concentrations (&#62;10%) and very low ionic strengths (salt concentration &#60;10 mM) may affect the image and data quality and are not recommended.</li>
	<li>Determine the actual concentrations of the antibody and antigen stocks by measuring their 280 nm UV absorbance.</li>
	<li>Calculate the measurement concentrations of the antigen-antibody mixture. If the estimated value of the antibody binding affinity is not known, plan to prepare a sample with 30 nM antigen and 20 nM antibody concentration. When the approximate affinity is known, the antibody to antigen ratio and their concentrations should be optimized according to the expected Kd values. Use the total antigen concentration in the mixture equal to the sum of the expected Kd and the total antibody concentration in the equation below. Assuming Kd1&#160;=&#160;Kd2 for the two paratopes of the antibody, this will result in comparable concentrations of the free antibody and the antibody-antigen complexes in the sample. 
	<img alt="Equation 4" src="/files/ftp_upload/61784/61784equ04v2.jpg" /> 
	Adjust the antibody concentration to keep the total protein concentration in the sample within the 10&#160;nM and 50 nM range. Best results are obtained using mixtures with antibody concentrations between 5 nM and 25 nM. 
	NOTE: MP detects proteins with a molecular mass larger than 40 kDa. Consequently, sample concentrations of antigens with a molecular mass smaller than 40&#160;kDa can exceed the typical 50 nM limit. However, at concentrations higher than approximately 100 nM, even low molecular mass antigens might affect the image quality and accuracy of the Kd determination.</li>
	<li>Prepare 50 &#181;L of the antibody-antigen mixture at its final measurement concentration calculated in step 2.3. 
	NOTE: Only one sample of the antigen-antibody mixture is required for the Kd determination. However, preparing several samples with different antigen to antibody ratios can help optimize sample concentration. If data from several samples are collected, they can be analyzed via a global fit.</li>
	<li>Incubate the antigen-antibody mixture(s) for approximately 10 min at room temperature to allow the binding reaction to reach chemical equilibrium. Avoid unnecessarily long incubation times. 
	NOTE: Incubation time may vary depending on the binding kinetics. To confirm that the chemical equilibrium has been reached, sample measurements can be repeated at different incubation times. Time-invariant Kd values indicate sufficiently long incubation. Prolonged incubation may lead to significant protein adsorption to the surface of the plastic labware and, consequently, to significant errors in the protein concentration determination. For this reason, low-adhesion labware is strongly recommended for MP sample preparation13.</li>
</ol>
3. Collect the Mass Photometry data
<ol>
	<li>Apply a drop of microscope immersion oil on the MP instrument objective and place the assembled flow chamber on the microscope stage. Make sure the oil spans the gap between the coverslip and the objective.</li>
	<li>Load the flow chamber and focus the mass photometer.
	<ol>
		<li>Deposit 10 &#181;L of a clean, filtered buffer solution at one end of the flow chamber channel prepared in step 1. Liquid will enter the channel by capillary action.</li>
		<li>Adjust the stage&#8217;s Z-position to focus the microscope on the working surface of the 24 x 50 mm coverslip.
		<ol>
			<li>In the Focus Control tab of the data collection software, use the coarse stage movement Up and Down buttons to make the initial adjustments.</li>
			<li>Click the Sharpness button to show the sharpness signal readout and use the fine Up and Down adjustment buttons to maximize the Sharpness value.</li>
			<li>Click the Set Focus and Lock Focus buttons to activate the focus tracking function. A properly focused image (Figure 1A,C) should have the &#8220;signal&#8221; value below 0.05%. 
			NOTE: If the &#8220;signal&#8221; value at the maximum sharpness position is above 0.05%, this may indicate impurities on the glass surface or in the buffer.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Using the same channel, load 20 &#181;L of the antibody-antigen sample by depositing it on one side of the channel and blotting the liquid from the other end with a small piece of blotting paper (Figure 2D). 
	NOTE: The volume of a 3&#8211;5 mm wide channel is approximately 10 &#181;L. The additional sample volume is recommended to completely replace the buffer present in the channel and to avoid sample dilution.</li>
	<li>After loading the sample, immediately click the Record button to start data collection, acquiring a 100 s video (Figure 1D).</li>
	<li>At the end of the data collection enter the file name and click OK to save the data file.</li>
	<li>Discard the coverslips and wipe the oil from the objective lens with cotton optical swabs wet with isopropanol. 
	NOTE: The protocol can be paused here.</li>
</ol>
4. Analyze the MP data
<ol>
	<li>Process the collected video file using the MP data processing software to identify the landing events.
	<ol>
		<li>Use the File/Open menu option to load the file for the analysis and click Analyze.</li>
		<li>Click the Load button to load the calibration function and save the analyzed data using the File/Save Results As menu option.</li>
	</ol>
	</li>
	<li>Fit the molecular mass distribution with Gaussian functions to obtain relative concentrations of each species in the sample. This analysis can be performed using a common scientific graphing software (see Table of Materials).
	<ol>
		<li>Import the &#8220;eventsFitted.csv&#8221; file into the software and plot the molecular mass distribution (column M in the .csv file) using the Plot/Statistics/Histogram function.</li>
		<li>Double click on the histogram to open the Plot Properties window. Disable automatic binning and select a bin size of 2.5 kDa. Click the Apply and the Go buttons to create the Bin Centers and Counts data.</li>
		<li>Select the Bin Centers and Counts columns and use the Analysis/Peaks and Baseline/Multiple Peak Fit menu function to fit the histogram with Gaussian functions. Double click to indicate the approximate peak positions on the distribution plot and then click the Open NLFit button.</li>
		<li>Check the Fixed checkboxes for the &#8220;xc&#8221; peak centers and set their values to the expected molecular masses of the free antibody and the single and double antigen-antibody complexes. Check the Share option for the width parameters. Click the Fit button. The fitted peak height values of the Gaussian components represent the relative concentration of each species in the sample11. 
		NOTE: Bin size may be adjusted to optimize the resolution of the mass distribution plot. The MP precision limit is approximately 1&#160;kDa, and smaller bin sizes might amplify the noise of the distribution, while not revealing any additional information. Very large bin sizes will obscure the fine details of mass distributions.</li>
	</ol>
	</li>
	<li>Calculate the concentration fraction of each species using the following equation: 
	<img alt="Equation 5" src="/files/ftp_upload/61784/61784equ05v2.jpg" /> 
	where the hi and fi values represent peak heights and concentration fractions of the free antibody and the single- and double-bound antibody in the sample, respectively.</li>
</ol>
5. Calculate equilibrium constant values
<ol>
	<li>Fit the concentration fractions of the interaction species calculated in step 4.3 with Eq. 1 and 2 using a suitable analytical software. Here we demonstrate a method to calculate equilibrium constants using a spreadsheet program14 (see Supplementary Information).
	<ol>
		<li>Open the &#8220;Kd calculation.xlsx&#8221; worksheet. In this worksheet, the cell values in rows 1 to 10 highlighted in yellow can be modified to perform the equilibrium constants calculations.</li>
		<li>Enter the estimated Kd values in nanomolar units into cells B1 and B2 in the table. Those starting values will be optimized in the fitting procedure. If the estimated Kd values are not known, leave the default values in cells B1 and B2 unchanged.</li>
		<li>Enter the values of (cAb)tot and (cAg)tot in nanomolar units into cells D2 and E2. Enter the fraction values calculated in step 4.3 into cells F2, G2, and H2. If multiple samples at different concentration ratios were measured, additional concentration values obtained for those samples can be entered in rows 2 to 10.</li>
		<li>Select the Data/Solver menu function. Enter &#8220;$B$15&#8221; in the &#8220;Set Objective&#8221; box and &#8220;$B$1:$B$2&#8221; into the &#8220;By Changing Variable Cells:&#8221; box. Select the Min radio button for the To: option. Check the Make Unconstrained Variables Non-Negative checkbox and select GRG Nonlinear as the solving method. Click the Solve button. The best fit Kd1 and Kd2 values will be shown in cell B1 and B2 and the final sum of squared errors in cell B15. 
		NOTE: If the Solver function is not active, select Options under the File menu in the spreadsheet program. In the Add-ins category select the Solver Add-in under the Inactive Application Add-ins and click the Go button. Check the Solver Add-in checkbox and click OK.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61784/61784fig01.jpg" /> 
Figure 1: Mass photometry images.&#160;(A) Representative native view image of the imaging buffer collected on a clean coverslip and (B) on a coverslip with surface imperfections. (C) Differential ratiometric image of the imaging buffer and (D) the AHT&#183;HT solution. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61784/61784fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61784/61784fig02.jpg" /> 
Figure 2: MP flow chamber preparation and loading.&#160;(A) Coverslip holding position for the cleaning procedure. (B) Alignment of the 24 x 24 mm coverslip (middle layer) and the double-sided tape (top layer) on the surface of aluminum foil (bottom layer, not shown). Blue dashed lines show the location of cut lines. (C) Top and side view of the assembled flow chamber with two sample channels, and a picture of the assembled flow chamber. (D) Procedure for sample loading into a flow channel previously filled with buffer. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61784/61784fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<ol>
	<li>Francis, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+phase+I+trial+of+antibody+directed+enzyme+prodrug+therapy+(ADEPT)+in+patients+with+advanced+colorectal+carcinoma+or+other+CEA+producing+tumours.">A phase I trial of antibody directed enzyme prodrug therapy (ADEPT) in patients with advanced colorectal carcinoma or other CEA producing tumours.</a> British Journal of Cancer. 87 (6), 600-607 (2002).</li><li>van Dyck, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anti-Amyloid-beta+Monoclonal+Antibodies+for+Alzheimer's+Disease:+Pitfalls+and+Promise.">Anti-Amyloid-beta Monoclonal Antibodies for Alzheimer's Disease: Pitfalls and Promise.</a> Biological Psychiatry. 83 (4), 311-319 (2018).</li><li>Vennepureddy, A., Singh, P., Rastogi, R., Atallah, J. P., Terjanian, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolution+of+ramucirumab+in+the+treatment+of+cancer+-+A+review+of+literature.">Evolution of ramucirumab in the treatment of cancer - A review of literature.</a> Journal of Oncology Pharmacy Practice. 23 (7), 525-539 (2017).</li><li>Waldmann, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immunotherapy:+past,+present+and+future.">Immunotherapy: past, present and future.</a> Nature Medicine. 9 (3), 269-277 (2003).</li><li>Huang, B., Bates, M., Zhuang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Super-resolution+fluorescence+microscopy.">Super-resolution fluorescence microscopy.</a> Annual Review of Biochemistry. 78, 993-1016 (2009).</li><li>Rosenberg, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Protein Analysis and Purification. , (2005).</li><li>Picot, J., Guerin, C. L., Le Van Kim, C., Boulanger, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry:+Retrospective,+fundamentals+and+recent+instrumentation.">Flow cytometry: Retrospective, fundamentals and recent instrumentation.</a> Cytotechnology. 64 (2), 109-130 (2012).</li><li>Khan, S. H., Farkas, K., Kumar, R., Ling, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+versatile+method+to+measure+the+binding+to+basic+proteins+by+surface+plasmon+resonance.">A versatile method to measure the binding to basic proteins by surface plasmon resonance.</a> Analytical Biochemistry. 421 (2), 385-390 (2012).</li><li>Lofgren, J. A., 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=Comparing+ELISA+and+surface+plasmon+resonance+for+assessing+clinical+immunogenicity+of+panitumumab.">Comparing ELISA and surface plasmon resonance for assessing clinical immunogenicity of panitumumab.</a> The Journal of Immunology. 178 (11), 7467-7472 (2007).</li><li>Young, G., 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=Quantitative+mass+imaging+of+single+biological+macromolecules.">Quantitative mass imaging of single biological macromolecules.</a> Science. 360 (6387), 423-427 (2018).</li><li>Wu, D., Piszczek, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+the+affinity+of+protein-protein+interactions+on+a+single-molecule+level+by+mass+photometry.">Measuring the affinity of protein-protein interactions on a single-molecule level by mass photometry.</a> Analytical Biochemistry. 592, 113575 (2020).</li><li>Cole, D., Young, G., Weigel, A., Sebesta, A., Kukura, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Label-free+single-molecule+imaging+with+numerical-aperture-shaped+interferometric+scattering+microscopy.">Label-free single-molecule imaging with numerical-aperture-shaped interferometric scattering microscopy.</a> ACS Photonics. 4 (2), 211-216 (2017).</li><li>Soltermann, F., 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=Quantifying+protein-protein+interactions+by+molecular+counting+with+mass+photometry.">Quantifying protein-protein interactions by molecular counting with mass photometry.</a> Angewandte Chemie International Edition in English. 59 (27), 10774-10779 (2020).</li><li>Kemmer, G., Keller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonlinear+least-squares+data+fitting+in+Excel+spreadsheets.">Nonlinear least-squares data fitting in Excel spreadsheets.</a> Nature Protocols. 5 (2), 267-281 (2010).</li></ol>

The authors have nothing to disclose.

The Mass Photometry based protocol outlined here provides a fast and accurate method of measuring antigen-antibody binding affinities. MP analysis uses a very small amount of material, and additional information&#8212;including stoichiometry, oligomerization, and purity&#8212;can be assessed from the same data (Figure 5). Without modifications, this method is applicable to the measurements of dissociation constants in the approximately 5 nM to 500 nM range, and for ligand molecules with molecular mass of approximately 20 kDa or larger. The same protocol can be used not only to analyze the antigen-antibody binding, but also to measure protein-protein interaction affinities when the molecular mass of at least one of the binding partners is larger than 50 kDa. Since the MP detection is non-specific, MP measurements can&#8217;t be performed on samples containing a high concentration of carrier proteins or large molecular mass impurities.
SPR is commonly used for the characterization of the antigen-antibody binding, and direct comparison of both methods may help with the assay selection. In comparison with SPR, the MP binding assay is faster and does not require protein immobilization. For a typical binding experiment, the MP requires less material than SPR. MP data reveal the stoichiometry of the complexes which is not readily available from the SPR10,11. Kinetic parameters of binding can be obtained from the MP data13, but SPR measures the binding kinetics directly, and is able to measure a broader range of association and dissociation rates. The association constants of two separate binding sites can only be obtained from the SPR data when the association and dissociation rates of both sites are sufficiently different. On the other hand, the MP can precisely characterize molecular complexes with multiple binding sites10,11. SPR is able to measure binding affinity for small molecular mass ligands, and MP works best for ligands with molecular mass of approximately 20 kDa or larger.
Collecting good quality MP data is a critical step in obtaining accurate results from this protocol. Impurities in the buffer or on the surface of the coverslips will interfere with MP data collection. Improper focusing distorts the MP signal leading to errors in molecular mass estimates and equilibrium constants calculations. Both these factors should be examined when troubleshooting the protocol. An important consideration when working with low concentration protein solutions is that surface adsorption can lead to the loss of material and changes in the sample concentration. The resulting errors can be minimized by using low adsorption labware and by applying surface passivation. To obtain accurate results, it is important to allow all protein dilutions and mixtures to reach chemical equilibrium, but unnecessarily long incubation times should be avoided. For each protein system, it is recommended that the rates of the non-specific protein binding to the coverslip glass be assessed from the MP data. If significantly different rates are observed for different species, MP data can easily be corrected to avoid potential errors in the Kd calculations10,11.
Several factors should be considered when planning sample preparation. Accurate results can be obtained by measuring a single sample, but the antigen and antibody concentrations have to be adjusted to obtain a comparable concentration of the free and bound species. Analysis of a single mass distribution dominated by one of the reaction species may provide acceptable Kd values estimates but usually yields relatively large fitting errors11. An alternative strategy involves a global analysis of titration data. The spreadsheet software based fitting tool provided with this protocol can be used for fitting both individual data and for global analysis. If a single experiment or data set is analyzed, the confidence intervals of best fit parameters can be estimated using the error projection method. A detailed description of the confidence intervals calculation procedure in the spreadsheet software is provided elsewhere14. When designing the assay, it is recommended to plan for replicate experiments. When data from replicate experiments are analyzed, standard deviation can be used to report errors of the Kd values.
The protocol described here can be modified to extend its applicability beyond the typical molecular mass and affinity range limitations. The range of measurable affinities is limited by the protein concentration range accessible by MP, typically from 10 nM to 50 nM. This range can be extended and protein samples at concentrations below 10 nM can be measured by using perfused flow cells and longer data acquisition times. Protein samples at higher concentrations can potentially be measured by using passivated coverslips for the MP measurements. For antigens with a small molecular mass, the free antibody and the antigen-antibody complex peaks in the MP mass distributions will be unresolved. This will preclude the use of the Gaussian peak fitting analysis described in the protocol. In that case, the binding affinity can still be measured by titrating the antibody with the antigen and averaging the MP distributions to obtain the average molecular mass of all species for each sample. The binding equation can then be fit to this data to obtain the antigen-antibody binding affinity11.
When accurate affinity information is not required, the protocol can be simplified and used for fast antibody interaction screening. In that case, the commercially available gasket wells can be used instead of sample channels to further simplify the experimental procedure.

Antibodies have become ubiquitous tools of molecular biology and are used extensively in both medical and research applications. In medicine, they are critically important in diagnostics, but their therapeutic applications are also expanding and new antibody-based therapies are constantly being developed1,2,3,4. The scientific applications of antibodies include many indispensable laboratory techniques such as immunofluorescence5, immunoprecipitation6, flow cytometry7, ELISA, and western blotting. For each of these applications, obtaining accurate measurements of the antibody&#8217;s binding properties, including binding affinity and specificity, is of crucial importance.
Since the first commercial surface plasmon resonance (SPR) instrument was introduced in 1990, optical biosensors have become the &#8220;gold standard&#8221; of antibody characterization, but other techniques, including ELISA, are also routinely used to measure antibody affinities8,9. These methods usually require immobilization or labeling of the analyzed molecules, which can potentially affect the interaction of interest. They are also relatively slow, involving multiple assay steps before the results can be collected for data analysis. A recently developed single-molecule method, mass photometry (MP), detects molecules directly in solution when they land on the surface of the microscope coverslip10,11. The light scattering-based optical detection that MP employs does not require protein labeling or modification. Individual protein molecules are recorded by the interferometric scattering microscope as dark spots appearing in the image (Figure 1D), and several thousand molecules can be detected during the one-minute data acquisition12. The signal generated by each individual particle is quantified, and its contrast value (relative darkness) is calculated. The interferometric contrast values are proportional to the molecular masses of the proteins, which allows for the identification of bound and free species in the antigen-antibody mixture. At the same time, by counting molecular landing events, MP directly measures the species populations. This gives MP based methods a unique capability to independently quantify affinities of multiple binding sites.
Binding of the antigen (Ag) molecules to the two binding sites of the intact antibody (Ab) can be described as: 
<img alt="Equation 1" src="/files/ftp_upload/61784/61784equ01v2.jpg" /> 
with the equilibrium association constants Ka1 and Ka2 defined as: 
<img alt="Equation 2" src="/files/ftp_upload/61784/61784equ02v5.jpg" /> 
where ci and fi represent concentration and fraction of the component i, respectively. The total antigen concentration (cAg)tot can be expressed as: 
<img alt="Equation 3" src="/files/ftp_upload/61784/61784equ03v3.jpg" />
Since the total concentrations of the antibody (cAb)tot and antigen (cAg)tot are known, this equation can be used to directly fit the experimental component fractions obtained from the MP measurements and calculate the equilibrium association constants Ka1 and Ka2 (see Supplementary Information).
The MP data can also be used to estimate cooperativity between the two antibody binding sites11. For two antibody paratopes with identical microscopic binding constants, the statistical factors describing the process of population of the Ab&#183;Ag and Ab&#183;Ag2 complexes dictate that the apparent macroscopic equilibrium constants Ka1 and Ka2 will not be numerically equal, and Ka1 = 4Ka2. Therefore, the experimental values of Ka1 &#60; 4Ka2 indicate positive cooperativity between the two antibody binding sites. Similarly, Ka1 &#62; 4Ka2 indicates negative cooperativity.
MP measurements of the antigen-antibody binding affinity are fast and require a small amount of material. The MP mass distributions used for equilibrium constant calculations provide additional information about the sample properties and enable the assessment of the sample purity, oligomerization, and aggregation in a single experiment. The same method can be used to measure high affinity protein-protein binding, and MP is particularly useful for studies of multi-valent protein interactions. Multi-protein complexes usually have large molecular masses, optimal for MP detection, and single-molecule data can be used to measure stoichiometry and calculate affinities of multiple binding sites simultaneously. This information is usually difficult to obtain using bulk-based methods.
Without modifications, the current protocol is suitable for measurements of relatively high-affinity, sub-micromolar interactions with antigens of a molecular mass of 20 kDa or larger. For optimal results, protein stocks should be of high purity, but there are no specific buffer requirements. By using MP, the antigen-antibody binding can be assessed in less than five minutes. The data collection and analysis required for accurate Kd calculations can be performed within 30 minutes.

<table><tbody><tr><td>AcquireMP</td><td>Refeyn</td><td></td><td>MP data collection software</td></tr><tr><td>Anti-human thrombin</td><td>Haematologic Technologies</td><td>AHT-5020</td><td>RRID: AB_2864302</td></tr><tr><td>Cotton-tipped applicators</td><td>Thorlabs</td><td>CTA10</td><td>cotton optical swabs for lens cleaning</td></tr><tr><td>Coverslips 24x24 mm</td><td>Globe Scientific</td><td>1405-10</td><td></td></tr><tr><td>Coverslips 24x50 mm</td><td>Fisher Scientific</td><td>12-544-EP</td><td></td></tr><tr><td>DiscoverMP</td><td>Refeyn</td><td></td><td>MP data processing software</td></tr><tr><td>Forceps</td><td>Electron Microscopy Sciences</td><td>78080-CF</td><td>soft-tipped forceps for coverslips handling</td></tr><tr><td>Human &amp;alpha;-thrombin</td><td>Haematologic Technologies</td><td>HCT-0020</td><td></td></tr><tr><td>Immersion oil</td><td>Thorlabs</td><td>MOIL-30</td><td></td></tr><tr><td>Isopropanol</td><td>Alfa Aesar</td><td>36644</td><td></td></tr><tr><td>Microsoft Excel</td><td>Microsoft</td><td></td><td>spreadsheet</td></tr><tr><td>OneMP</td><td>Refeyn</td><td></td><td>Mass Photometry instrument</td></tr><tr><td>Origin</td><td>OriginLab</td><td></td><td>scientific graphing software</td></tr><tr><td>PBS</td><td>Corning</td><td>46-013-CM</td><td>10x stock</td></tr><tr><td>Syringe filter</td><td>Millipore</td><td>SLGSR33SS</td><td>buffer and sample filtering</td></tr></tbody></table>

rapid determination of antibody-antigen affinity by mass photometry

Measurements of the specificity and affinity of antigen-antibody interactions are critically important for medical and research applications. In this protocol, we describe the implementation of a new single-molecule technique, mass photometry (MP), for this purpose. MP is a label- and immobilization-free technique that detects and quantifies molecular masses and populations of antibodies and antigen-antibody complexes on a single-molecule level. MP analyzes the antigen-antibody sample within minutes, allowing for the precise determination of the binding affinity and simultaneously providing information on the stoichiometry and the oligomeric state of the proteins. This is a simple and straightforward technique that requires only picomole quantities of protein and no expensive consumables. The same procedure can be used to study protein-protein binding for proteins with a molecular mass larger than 50 kDa. For multivalent protein interactions, the affinities of multiple binding sites can be obtained in a single measurement. However, the single-molecule mode of measurement and the lack of labeling imposes some experimental limitations. This method gives the best results when applied to measurements of sub-micromolar interaction affinities, antigens with a molecular mass of 20 kDa or larger, and relatively pure protein samples. We also describe the procedure for performing the required fitting and calculation steps using basic data analysis software.

We have previously examined the interaction of human &#945;-thrombin (HT) and mouse monoclonal anti-human thrombin antibody (AHT) using the MP based assay11. Since the molecular mass of the HT (37&#160;kDa) is below the 40 kDa detection limit, the maximum sample concentration can exceed the 50 nM MP concentration limitation without negatively affecting the resolution of mass distributions. The experiment was planned as a titration series with the AHT antibody at a fixed 25&#160;nM concentration, and the HT at concentrations of 7.5 nM, 15 nM, 30 nM, 60 nM and 120 nM. Figure 3 shows the molecular mass distributions of the antigen-antibody mixtures and the antibody-only sample. Here we analyze the data using the method described in the protocol. A scientific graphing software was used to fit the mass distributions with three Gaussian components representing the free AHT, AHT&#183;HT and AHT&#183;HT2. The known molecular mass values of the three components were fixed, and a single peak width parameter was fitted for the three species. Best-fit peak height parameters of the Gaussian components were normalized using Eq. 3 to obtain species concentration fractions (Table 1). Those values, together with the total antibody and antigen concentration for each sample were entered into the &#8220;Kd calculation.xlsx&#8221;. The global fit in the spreadsheet yields Kd1 = 40 nM (68.3% confidence interval: 28&#160;nM, 68&#160;nM) and Kd2 = 28 nM (68.3% confidence interval: 17&#160;nM, 45&#160;nM). The experimental concentration fractions and the fit results are plotted in Figure 4. The dissociation constant values obtained here by fitting the integrated concentration fractions are in agreement with those obtained previously by directly fitting MP distributions, and with dissociation constant values obtained by Isothermal Titration Calorimetry (ITC)11.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61784/61784fig3v2.jpg" /> 
Figure 3: MP molecular mass distributions of the 25 nM AHT mixed with HT at 0, 7.5, 15, 30, 60 and 120 nM&#160;(A-F, respectively). Black dots show the experimental MP data plotted with 2.5 kDa bin size. Cyan, green, and blue lines represent the best-fit Gaussian distributions of the free antibody, single bound antibody, and double bound antibody species, respectively. Red lines show the sum of the three Gaussian components. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61784/61784fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61784/61784fig04.jpg" /> 
Figure 4: Fractions of the free AHT (blue), AHT&#183;HT (red), and AHT&#183;HT2 (black) as a function of HT concentration.&#160;Points represent experimental values obtained from the Gaussian fitting of the MP distributions. Solid lines represent the best-fit using Eq. 1 and Eq. 2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61784/61784fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61784/61784fig05.jpg" /> 
Figure 5: Technical replicates of the AHT molecular mass distribution measurements.&#160;Plots shows the reproducibility of the MP measurements and the purity of the antibody preparation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61784/61784fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ab conc (M)</td>
			<td>Ag conc (M)</td>
			<td>f_Ab</td>
			<td>f_AbAg</td>
			<td>f_AbAg2</td>
		</tr>
		<tr>
			<td>2.50E-08</td>
			<td>7.50E-09</td>
			<td>0.88</td>
			<td>0.10</td>
			<td>0.02</td>
		</tr>
		<tr>
			<td>2.50E-08</td>
			<td>1.50E-08</td>
			<td>0.81</td>
			<td>0.15</td>
			<td>0.04</td>
		</tr>
		<tr>
			<td>2.50E-08</td>
			<td>3.00E-08</td>
			<td>0.72</td>
			<td>0.21</td>
			<td>0.07</td>
		</tr>
		<tr>
			<td>2.50E-08</td>
			<td>6.00E-08</td>
			<td>0.27</td>
			<td>0.37</td>
			<td>0.35</td>
		</tr>
		<tr>
			<td>2.50E-08</td>
			<td>1.20E-07</td>
			<td>0.05</td>
			<td>0.23</td>
			<td>0.72</td>
		</tr>
	</tbody>
</table>
Table 1: Normalized peak heights of Gaussian components obtained by fitting the MP distributions (Figure 3).
Supplementary Information: Implementation of the equilibrium constants fitting procedure in Excel and Affinity calculation worksheet. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61784/Supplemental_Files.zip" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Rapid Determination of Antibody-Antigen Affinity by Mass Photometry at JoVE.com

Rapid Determination of Antibody-Antigen Affinity by Mass Photometry

We describe a single-molecule approach to antigen-antibody affinity measurements using mass photometry (MP). The MP-based protocol is fast, accurate, uses a very small amount of material, and does not require protein modification.

Measurements of the specificity and affinity of antigen-antibody interactions are critically important for medical and research applications. In this ...

rapid-determination-of-antibody-antigen-affinity-by-mass-photometry

Nanyang Technological University

Research

Education

JoVE Journal

JoVE Core

Cell Biology

Biochemistry

Unit 1

Energy and Catalysis

SCIENTISTS IN ACTION

7.4K Views.  National Institutes of Health. Antilichamen worden routinematig gebruikt in laboratoriumtechnieken en hun therapeutische en diagnostische toepassingen breiden zich snel uit. Snelle en nauwkeurige antigeen-antilichaam bindende karakterisering is van cruciaal belang voor deze toepassingen. Massafotometrie meet snel bindende affiniteiten met extreem kleine hoeveelheden materiaal.Er is geen etikettering of immobilisatie vereist en informatie over eiwitreligie en zuiverheid kan worden verkregen uit hetzelfde experiment. ...

Video: Rapid Determination of Antibody-Antigen Affinity by Mass Photometry

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

We describe a simple protocol for identifying and quantifying the two components in binary mixtures of species possessing one or more similar proteins. Central to the method is the identification of 'corresponding proteins' in the species of interest, in other words proteins that are nominally the same but possess species-specific sequence differences. When subject to proteolysis, corresponding proteins will give rise to some peptides which are likewise similar but with species-specific variants. These are 'corresponding peptides'. Species-specific peptides can be used as markers for species determination, while pairs of corresponding peptides permit relative quantitation of two species in a mixture. The peptides are detected using multiple reaction monitoring (MRM) mass spectrometry, a highly specific technique that enables peptide-based species determination even in complex systems. In addition, the ratio of MRM peak areas deriving from corresponding peptides supports relative quantitation. Since corresponding proteins and peptides will, in the main, behave similarly in both processing and in experimental extraction and sample preparation, the relative quantitation should remain comparatively robust. In addition, this approach does not need the standards and calibrations required by absolute quantitation methods. The protocol is described in the context of red meats, which have convenient corresponding proteins in the form of their respective myoglobins. This application is relevant to food fraud detection: the method can detect 1% weight for weight of horse meat in beef. The corresponding protein, corresponding peptide (CPCP) relative quantitation using MRM peak area ratios gives good estimates of the weight for weight composition of a horse plus beef mixture.

We present a protocol for identifying and quantifying the components in mixtures of species possessing similar proteins. Mass spectrometry detects peptides for identification, and gives relative quantitation by ratios of peak areas. As a tool food for fraud detection, the method can detect 1% horse in beef.

We describe a simple protocol for identifying and quantifying the two components in binary mixtures of species possessing one or more similar proteins. ...

A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties

Plants can produce valuable substances such as secondary metabolites and recombinant proteins. The purification of the latter from plant biomass can be streamlined by heat treatment (blanching). A blanching apparatus can be designed more precisely if the thermal properties of the leaves are known in detail, i.e., the specific heat capacity and thermal conductivity. The measurement of these properties is time consuming and labor intensive, and usually requires invasive methods that contact the sample directly. This can reduce the product yield and may be incompatible with containment requirements, e.g., in the context of good manufacturing practice. To address these issues, a non-invasive, contact-free method was developed that determines the specific heat capacity and thermal conductivity of an intact plant leaf in about one minute. The method involves the application of a short laser pulse of defined length and intensity to a small area of the leaf sample, causing a temperature increase that is measured using a near infrared sensor. The temperature increase is combined with known leaf properties (thickness and density) to determine the specific heat capacity. The thermal conductivity is then calculated based on the profile of the subsequent temperature decline, taking thermal radiation and convective heat transfer into account. The associated calculations and critical aspects of sample handling are discussed.

A method was developed to determine the specific heat capacity and thermal conductivity of leaf tissue by non-invasive, contact-free near infrared laser probing, which requires less than 1 min per sample.

Plants can produce valuable substances such as secondary metabolites and recombinant proteins. The purification of the latter from plant biomass can be ...

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Label-free optical biosensors are powerful tools in drug discovery for the characterization of biomolecular interactions. In this study, we describe the use of four routinely used biosensor platforms in our laboratory to evaluate the binding affinity and kinetics of ten high-affinity monoclonal antibodies (mAbs) against human proprotein convertase subtilisin kexin type 9 (PCSK9). While both Biacore T100 and ProteOn XPR36 are derived from the well-established Surface Plasmon Resonance (SPR) technology, the former has four flow cells connected by serial flow configuration, whereas the latter presents 36 reaction spots in parallel through an improvised 6 x 6 crisscross microfluidic channel configuration. The IBIS MX96 also operates based on the SPR sensor technology, with an additional imaging feature that provides detection in spatial orientation. This detection technique coupled with the Continuous Flow Microspotter (CFM) expands the throughput significantly by enabling multiplex array printing and detection of 96 reaction sports simultaneously. In contrast, the Octet RED384 is based on the BioLayer Interferometry (BLI) optical principle, with fiber-optic probes acting as the biosensor to detect interference pattern changes upon binding interactions at the tip surface. Unlike the SPR-based platforms, the BLI system does not rely on continuous flow fluidics; instead, the sensor tips collect readings while they are immersed in analyte solutions of a 384-well microplate during orbital agitation.
Each of these biosensor platforms has its own advantages and disadvantages. To provide a direct comparison of these instruments&#39; ability to provide quality kinetic data, the described protocols illustrate experiments that use the same assay format and the same high-quality reagents to characterize antibody-antigen kinetics that fit the simple 1:1 molecular interaction model.

We describe here protocols for the measurement of antibody-antigen binding affinity and kinetics using four commonly used biosensor platforms.

Label-free optical biosensors are powerful tools in drug discovery for the characterization of biomolecular interactions. In this study, we describe the ...

The Determination of Protease Specificity in Mouse Tissue Extracts by MALDI-TOF Mass Spectrometry: Manipulating PH to Cause Specificity Changes

Proteases have several biological functions, including protein activation/inactivation and food digestion. Identifying protease specificity is important for revealing protease function. The method proposed in this study determines protease specificity by measuring the molecular weight of unique substrates using Matrix-assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry. The substrates contain iminobiotin, while the cleaved site consists of amino acids, and the spacer consists of polyethylene glycol. The cleaved substrate will generate a unique molecular weight using a cleaved amino acid. One of the merits of this method is that it may be carried out in one pot using crude samples, and it is also suitable for assessing multiple samples. In this article, we describe a simple experimental method optimized with samples extracted from mouse lung tissue, including tissue extraction, placement of digestive substrates into samples, purification of digestive substrates under different pH conditions, and measurement of the substrates&#39; molecular weight using MALDI-TOF mass spectrometry. In summary, this technique allows for the identification of protease specificity in crude samples derived from tissue extracts using MALDI-TOF mass spectrometry, which may easily be scaled up for multiple sample processing.

Here, we present a protocol to determine protease specificity in crude mouse tissue extracts using MALDI-TOF spectrometry.

Proteases have several biological functions, including protein activation/inactivation and food digestion. Identifying protease specificity is important ...

Leaf Spray Mass Spectrometry: A Rapid Ambient Ionization Technique to Directly Assess Metabolites from Plant Tissues

Plants produce thousands of small molecules that are diverse in their chemical properties. Mass spectrometry (MS) is a powerful technique for analyzing plant metabolites because it provides molecular weights with high sensitivity and specificity. Leaf spray MS is an ambient ionization technique where plant tissue is used for direct chemical analysis via electrospray, eliminating chromatography from the process. This approach to sampling metabolites allows for a wide range of chemical classes to be detected simultaneously from intact plant tissues, minimizing the amount of sample preparation needed. When used with a high-resolution, accurate mass MS, leaf spray MS facilitates the rapid detection of metabolites of interest. It is also possible to collect tandem mass fragmentation data with this technique to facilitate a compound identification. The combination of accurate mass measurements and fragmentation is beneficial in confirming compound identities. The leaf spray MS technique requires only minor modifications to a nanospray ionization source and is a useful tool to further expand the capabilities of a mass spectrometer. Here, fresh leaf tissue from Sceletium tortuosum (Aizoaceae), a traditional medicinal plant from South Africa, is analyzed; numerous mesembrine alkaloids are detected with leaf spray MS.

Leaf spray mass spectrometry is a direct chemical analysis technique that minimizes the sample preparation and eliminates chromatography, allowing for the rapid detection of small molecules from plant tissues.

Plants produce thousands of small molecules that are diverse in their chemical properties. Mass spectrometry (MS) is a powerful technique for analyzing ...

A Python-Based Tool for Accurate Mass Calculation of Biotherapeutic Entities

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

Biotherapeutic masses are a means of verifying identity and structural integrity. Mass spectrometry (MS) of intact proteins or protein subunits provides an easy analytical tool for different stages of biopharmaceutical development. The protein's identity is confirmed when the experimental mass from MS is within a pre-defined mass error range of the theoretical mass. While several computational tools exist for the calculation of protein and peptide molecular weights, they either were not designed for direct application to biotherapeutic entities, have access limitations due to paid licenses, or require uploading protein sequences to host servers. 
We have developed a modular mass calculation routine that enables the easy determination of the average or monoisotopic masses and elemental compositions of therapeutic glycoproteins, including monoclonal antibodies (mAb), bispecific antibodies (bsAb), and antibody-drug conjugates (ADC). The modular nature of this Python-based calculation framework will allow the extension of this platform to other modalities such as vaccines, fusion proteins, and oligonucleotides in the future, and this framework could also be useful for the interrogation of top-down mass spectrometry data. By creating an open-source standalone desktop application with a graphical user interface (GUI), we hope to overcome the restrictions around use in environments where proprietary information cannot be uploaded to web-based tools. This article describes the algorithms and application of this tool, mAbScale, to different antibody-based therapeutic modalities.

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

This article describes the use of a software application, mAbScale, for the calculation of masses for monoclonal antibody-based protein therapeutics.

Biotherapeutic masses are a means of verifying identity and structural integrity. Mass spectrometry (MS) of intact proteins or protein subunits provides ...

Expanding the Mass Photometry Analysis Concentration Range with Microfluidics

Analysis of Protein Complex Formation at Micromolar Concentrations by Coupling Microfluidics with Mass Photometry

Mass photometry is a versatile mass measurement technology that enables the study of biomolecular interactions and complex formation in solution without labels. Mass photometry is generally suited to analyzing samples in the 100 pM-100 nM concentration range. However, in many biological systems, it is necessary to measure more concentrated samples to study low-affinity or transient interactions. Here, we demonstrate a method that effectively expands the range of sample concentrations that can be analyzed by mass photometry from nanomolar to tens of micromolar. 
In this protocol, mass photometry is combined with a novel microfluidics system to investigate the formation of protein complexes in solution in the micromolar concentration range. With the microfluidics system, users can maintain a sample at a desired higher concentration followed by dilution to the nanomolar range - several milliseconds prior to the mass photometry measurement. Due to the speed of the dilution, data is obtained before the equilibrium of the sample has shifted (i.e., dissociation of the complex). 
The technique is applied to measure interactions between an immunoglobulin G (IgG) antibody and the neonatal Fc receptor, showing the formation of high-order complexes that were not quantifiable with static mass photometry measurements. 
In conclusion, the combination of mass photometry and microfluidics makes it possible to characterize samples in the micromolar concentration range and is proficient in measuring biomolecular interactions with weaker affinities. These capabilities can be applied in a range of contexts - including the development and design of biotherapeutics - enabling thorough characterization of diverse protein-protein interactions.

Author Spotlight: Characterization of Low-Affinity Protein Interactions in Solution Using MassFluidix Technology

This protocol combines mass photometry with a novel microfluidics system to investigate low-affinity protein-protein interactions. This approach is based on the rapid dilution of highly concentrated complexes in solution, which enables low-affinity measurements and broadens the applicability of mass photometry.

Mass photometry is a versatile mass measurement technology that enables the study of biomolecular interactions and complex formation in solution without ...

Photometric Protein Determination

Measuring the concentration is a fundamental step of many biochemical assays. Photometric protein determination takes advantage of the fact that the more a sample contains light-absorbing substances, the less the light will transmit through it. Since the relationship between concentration and absorption is linear, this phenomenon can be used to measure the concentration in samples where it is unknown.
This video describes the basics of photometric protein determination and introduces the Bradford Assay and the Lowry Method. The procedure in the video will cover a typical Bradford assay. Applications covered include direct measurement of very small volumes of nucleic acids to characterize concentration and purity, determination of coupling efficiency of a biomimetic material, and another variation of photometric protein determination using Remazol dye.
<div class="chapter_text" id="script_0">
	Determining the concentration of a protein in samples is a fundamental step in many biochemical assays. Photometric determination can be done with small sample sizes. The more a sample contains light-absorbing substances, the less the light will transmit through it. This provides a quantitative measurement of the absorbing substances. These concepts are so fundamental to science that the articles that introduced two of the techniques are in the three most cited papers of all time. This video will show the concepts behind some of the most common photometric protein determination techniques, how they are performed, and how the gathered data is analyzed.
</div>
<div class="chapter_text" id="script_1">
	Photometric protein determination is based on the relationship between concentration and light absorbency. This is known as the Beer-Lambert Law, which states that the concentration of a light-absorbing species is proportional to its absorbance.
	This principle underlies all photometric protein determination methods. 
	For direct absorption analysis, the absorbance values of unaltered protein samples are measured. Because of their aromatic side chains, tryptophan and tyrosine residues give the highest absorbance readings at a wavelength of 280 nm.
	However, these amino acids-which are two of the least frequently found in proteins-are present in different amounts in every protein, so each determination is unique. To overcome this limitation, more complex assays-that are not dependent on these amino acids-were developed.
	One example is the Bradford Assay, where colored dye is added to the sample. The dye, known as Coomassie Blue, responds proportionally-the more protein present, the more binding events with the dye.
	Then, protein concentration is determined by measuring the absorbance of the bound Coomassie Blue dye, which absorbs light at 594 nm. However, the Bradford assay is linear over a short range of concentrations, so dilutions are often required before analysis.
	The Lowry Method combines the Biuret reagent, an alkaline solution of copper ions that react with peptide bonds, and the Folin-Cioc&#226;lteu reagent, which oxidizes aromatic protein residues. The resulting color change of the sample is proportional to the protein concentration.
	The absorbance of the reduced Folin reagent can be determined at 750 nm. Like direct absorption, each protein has a unique response, and must be calibrated for the protein of interest. Now that we've reviewed the basic principles behind some of the most common assays, let's look at how direct absorption and the Bradford assay are performed.
</div>
<div class="chapter_text" id="script_2">
	To begin a direct absorption analysis, the spectrophotometer is calibrated with a blank to determine zero absorbance. Standard solutions are prepared for use in creating the calibration curve. Then, an aliquot of the first standard is added to a cuvette, and placed into the spectrophotometer.
	The absorbance value at 280 nm is then recorded. This process is repeated for each standard, using a clean cuvette for each run. Once complete, a calibration curve is created by plotting the absorbance versus concentration. The slope of this line is the molar attenuation coefficient, which relates absorbance to concentration.
	Next, the unknown sample is added to a cuvette, and the absorbance value is recorded. As the data analysis for the different photometric determination methods is similar, we will cover that after we look at the Bradford assay.
	Here, the Bradford protein assay is performed with a BSA standard on a 96-well plate. To begin, BSA stock solutions are prepared.
	The unknown solutions are diluted with deionized water to ensure that the concentrations are within the assay's range. Depending on the kit, the Coomassie dye may also require dilution. Then, the calibration curve is set up by adding the BSA standards to the 96-well plate.
	Deionized water is added to reach the needed concentration to generate a standard curve. The unknown sample should be added to the plate in triplicates to ensure an accurate measurement is taken. Coomassie dye is next added to each well, mixing with the pipette.
	Deionized water is added to an empty well as a blank, to measure the absorbance. After waiting 5 min for the dye to bind, the absorbance is measured in a plate-reader at 590 nm.
</div>
<div class="chapter_text" id="script_3">
	Now that we've performed a few assays, let's look at how to analyze the data. Each photometric protein determination method is based on the Beer-Lambert Law.
	The measured absorbance of the standards is used to create a calibration curve, which is then used to determine the concentration of unknown samples. This curve can be manually plotted, though newer spectrophotometric tools will create the calibration curve once all standards have been measured. These systems will also calculate protein concentration as unknown samples are analyzed.
</div>
<div class="chapter_text" id="script_4">
	Now that we've reviewed how to analyze photometric protein determination data, let's look at some of the ways these procedures are utilized.
	The principles of photometric protein determination can also be used to directly measure nucleic acid concentration. The nanodrop spectrophotometer accepts samples of very small volume onto an optically active pedestal. The absorbance is then measured, and the system automatically determines the nucleic acid concentration. Because proteins and other sources can interfere with measurements, sample purity is determined by analyzing the 260 to 280 nm and 260 to 230 nm absorbance ratios. Pure nucleic acids typically yield ratios of approximately 1.8 and approximately 2.0 for DNA and RNA, respectively.
	Photometric protein determination can also be used in the production of biomimetic materials, which are inspired from nature to elicit specific cellular responses. Recombinant adhesins are bound to polystyrene beads to simulate bacterial attachment to host cells. The Bradford assay is used to determine the coupling efficiency of the recombinant adhesion to the beads in the production of the biomimetic material.
	Alternative photometric protein assays can be used in the detection and characterization of protein antimicrobials. Remazol brilliant blue R dye is covalently bonded to heat-killed bacteria. The protein antimicrobial is incubated in the dyed solution. Then, the sample is centrifuged, and the absorbance of the supernatant at 595 nm is measured using a microplate spectrophotometer. Increased absorbance, by the soluble dye released into the supernatant from the labeled bacteria, is a quantitative measurement of enzymatic activity.
</div>
<div class="chapter_text" id="script_5">
	You've just watched JoVE's video on photometric protein determination. This video described the underlying principles of photometric determination, went over general procedures for some common assays, and covered some new advances in techniques. Thanks for watching!
</div>

Photometric Protein Determination: Bradford Assay

Measuring the concentration is a fundamental step of many biochemical assays. Photometric protein determination takes advantage of the fact that the more ...

JoVE Science Education

Chemistry

Mass Photometry to Study Antigen-Antibody Interactions on a Single-Molecule Level

Source: Wu, D. et. al., <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/61784">Rapid Determination of Antibody-Antigen Affinity by Mass Photometry.</a> J. Vis. Exp.&#160;(2021)
In this video, we demonstrate the use of mass photometry to study antigen-antibody interactions in a flow chamber. The focused light scatters differently based on the mass of the molecules. A detector collects the scattered light and generates the interference pattern as spots of varied intensities, which correlate with the molecular masses.

Source: Wu, D. et. al., Rapid Determination of Antibody-Antigen Affinity by Mass Photometry. J. Vis. Exp.&#160;(2021)
In this video, we demonstrate the ...

Rapid Determination of Antibody-Antigen Affinity by Mass Photometry

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video