The authors are very thankful to Joseph Mancini (formerly with GE Healthcare), Pawel Czudec, Thomas Cage (Malvern Instruments limited) for their role in the installation and training on the differential scanning calorimeter, Sasmit Deshmukh and Webster Magcalas for their discussions.

1. Instrument Start-up
<ol>
	<li>Switch on the differential scanning calorimeter and increase the pressure in the cells to suppress boiling of the samples as well as prevent the formation bubbles at elevated temperatures. This is typically achieved by supplying nitrogen into the system.</li>
	<li>Depending on the constituting material of the cell (e.g., tantalum, gold, platinum, etc.), adjust the pressure of the nitrogen gas supply according to the manufacturer&#39;s recommended pressure to avoid damaging the cell. For example, set the pressure of the nitrogen gas supply to 45 psi for the instrument used to develop this procedure, and pressure above 80 psi may damage the cell.</li>
	<li>Ensure that all the cleaning agent reservoirs are filled to the required volume. Cleaning agents required include detergent and water to wash and clean the cell respectively after each sample run.</li>
	<li>Set the temperature of the sample holding compartment to a suitable value, preferably 5 &#176;C, to maintain the integrity of the sample prior to experiment.</li>
</ol>
2. Sample Preparation
<ol>
	<li>Dialyze the sample against the buffer that will be used as the reference for the experiment. Alternatively, elution buffer collected at the final step of protein purification (i.e. column elution) can be used.</li>
	<li>Determine the concentration of the protein sample using the most suitable protein concentration determination method such as Kjedahl method 33 or Lowry method 34. For objective comparison of results, use the same method consistently within the same study. The required concentration range may vary depending on the model of the instrument. For the instrument used in this protocol, the preferable working range is 0.5 &#8211; 1 mg/mL.</li>
	<li>Degas the sample and reference buffer in vacuum to get rid of microbubbles that can cause volume inaccuracy. This step can be skipped for newer calorimeter models.</li>
	<li>Using a micropipette and sterile tips in a laminar flow biocontainment cabinet, load the samples and their respective buffer in pairs into 96 well plates compatible with the instrument. Fill the first two pairs of wells with buffer and the last two pairs with water for the buffer-buffer and the water scan respectively. Buffer-buffer scans verify the suitability of the instrument prior to sample measurement (i.e. assessment of instrumentation error) as well as establish a baseline; while water scans are run to clean the cells.</li>
	<li>Cover the 96-well plate with a sealing film, and ensure that the wells are properly sealed before taking the plate out of the Biosafety Cabinet to avoid sample contamination.</li>
	<li>Place the plate in the sample holding compartment in the proper orientation.</li>
</ol>
3. Experimental Parameter Setup
Note: Depending on the instrumentation, samples can be loaded into the cell either manually using a syringe, or automatically using an autosampler. In this case (i.e. an industrial setting), an autosampler is used to save time.
<ol>
	<li>Using the acquisition software, enter the sample information in the order the plate was loaded as per section 2.4.&#160; Enter concentrations if available, otherwise, enter concentration values into analysis software prior to data analysis (section 4.2).</li>
	<li>Select the option that ensures cleaning of cells with detergent before every sample scan, which should be followed by multiple water rinse steps to ensure no detergent residue is left in the cells.</li>
	<li>Set the starting temperature of the experiment to 20 &#176;C, but this can vary depending on prior knowledge of the sample. For known proteins, pre-determined starting temperature can be used, while a lower starting temperature can be applied for unknown samples.</li>
	<li>Set the final temperature of the experiment, e.g. 100 &#176;C. The final temperature may vary depending on prior knowledge of the sample.</li>
	<li>Set the scan rate of the experiment, e.g. 60 &#176;C/h, which is the typical scan rate. However, scan rate may vary depending on prior knowledge of the sample, e.g. 90 &#176;C/h, or 120 &#176;C/h. It is advisable to scan unknown samples at different scan rates to assess the kinetics of unfolding.</li>
	<li>&#160;Rescan samples to investigate the reversibility of the thermal unfolding. The unfolding of a protein is considered reversible if the enthalpy obtained for the second scan is at least 80% of the enthalpy value for the first scan.</li>
	<li>Set the post-experiment thermostat to 10 &#176;C to preserve the integrity of the calorimeter&#39;s cells.</li>
	<li>Verify that the experiment setup parameters are correct before executing the experiment. If everything is in place, start the experiment.</li>
</ol>
4. Data Analysis
<ol>
	<li>Retrieve raw data from the experiment and select one sample at a time for analysis. Subtract reference scan, i.e. buffer, from the sample scan. 
	NOTE: Reference subtraction is carried out automatically by newer models of DSC instruments.</li>
	<li>Enter sample concentration value if it was omitted as per section 3.1.</li>
	<li>Fit and subtract baseline from the acquired thermogram to account for differences in the heat capacities of the folded and unfolded states of the protein which is caused by the exposure of hydrophobic groups to water upon unfolding. Linear or cubic curve fitting can be applied depending on the shape of the DSC profile for the sample. For consistency, the same type of fitting has to be used during a study, e.g. real-time stability study. This step is required to process the curve for peak integration to obtain enthalpy of transition.</li>
	<li>Perform peak integration using non-linear least square fit. Based on product knowledge apply two-state or non-two-state model. Two-state model can be used for single cooperative thermal transitions, and for unknown proteins, apply non-two state model until further product knowledge is available. If applicable, adjust curve fitting using the iterative curve fitting function of the equipment&#39;s software until the Chi-square value remains constant.</li>
	<li>Obtained results will show the values for midpoint of transition temperatures (Tm), calorimetric enthalpy (&#916;H), and van&#8217;t Hoff enthalpy (&#916;HVH) of the sample.</li>
</ol>

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All authors are employees at Sanofi Pasteur. The work was funded by Sanofi Pasteur, and publication fees for this video-article were paid by Sanofi Pasteur.&#160; The authors have no relevant affiliations or financial involvement with any organization or entity that has a financial conflict with the subject matter or materials discussed in the manuscript. These include employment, consultancies, stock ownership or options, or royalties.
No writing assistance was utilized in the production of this manuscript.

This procedure has been successfully incorporated in various characterization test packages, including stability and product comparability studies 21. In real-time stability studies, DSC is used to monitor the Tm, as well as estimate the Fa of biologics over time to determine their shelf-life. With regards to product comparability, it is used to assess the impact of process and facility change as well as the effect of key manufacturing steps on the structural conformation of produced lots. This typically involves the direct comparison of the &#916;H of produced lot to a reference product that has been designated as the ideal product. In addition, DSC has proven to be a useful analytical tool for product formulation studies 37. The Tm of a protein in different buffers and at different concentrations can be used to determine the formulation that proffers the most stability to the protein.
To ensure the reliability of this method and objectivity of its results, it is important to keep testing parameters consistent from run to run within the same study (e.g., vaccine formulation study). However, the procedure can be modified to accommodate differences in the physical properties of various proteins. An example of a modification that can be made is altering the scanning rate of the experiment 38, 39. Proteins that were prone to forming aggregates when heated were examined at a faster scanning rate (e.g., 120 &#176;C/h) to avoid the contribution of aggregates to the thermal transition profile as well as clogging the capillaries of the calorimeter. It is worth noting that scanning rate can influence the outcome of a DSC experiment 38. Broadening of the thermal transition peak has been observed with increasing scanning rates in some proteins; however, Tm remained fairly constant 38. Furthermore, the dialysis and degassing steps for sample preparation are also very crucial for accurate results 31. Dialysis ensures that the only difference in the composition of the sample and the buffer is the protein; thus, all the excess heat absorbed by the sample can be attributed to the heat capacity of the protein. Degassing ensures precise volume analysis, as the extrapolation of the thermodynamic parameter assumes that the unfolding event is occurring under constant volume and pressure 31. The constant pressure portion of the assumption is accounted for by nitrogen pressurization of the system as per section 1.1 of the procedure.
In comparison to other methods of determining the stability of protein conformations such as Circular Dichroism (CD) and Fluorescence Spectroscopies, DSC offers a number of advantages in a commercial setting including cost and time savings. Firstly, the adiabatic design of a differential scanning calorimeter allows for the measurement of thermal stability with better temperature precision as compared to measurements with instrumentation for CD and Fluorescence Spectroscopies 6. Secondly, unlike CD, the accuracy of DSC data is not dependent on the helicity of the protein 39, 40; however, CD provides additional information about the unfolding of the secondary structure, which would be complimentary to the DSC 41. Additionally, the pressurization of the DSC system allows for testing with a wide temperature range without boiling the sample; thus, a wide range of proteins can be tested by DSC.
While DSC is a relatively fast and straightforward approach to determine the thermal stability of biologics, it is not without limitations. First, the baseline subtraction step introduces some form of human inconsistency into the raw data analysis; thus, variations in results may be observed among different users. Second, differential scanning calorimeters have minimum concentration limits which might be difficult to achieve at bulk manufacturing scale. Third, the &#916;H of irreversible thermal denaturation is not absolute; which implies that derived &#916;G (an indicator of protein stability) in similar scenarios can be misleading. Furthermore, the method works best for purified samples. Presence of impurities may either cause a shift in the Tm if there is an interaction with the protein under investigation, or appearance of new thermal transitions if there is no interaction. In any case these extra features on the thermograms can be wrongly attributed to samples, thus impacting the interpretation of results. Despite these limitations, DSC remains a reliable method that can provide detailed thermodynamic information about the protein unfolding process if implemented properly 42.
In conclusion, DSC offers considerable advantage as a conformational readout tool for vaccine products and their intermediates. The two parameters, Tm and &#916;H, collected for an array of lots of the same product can become an empirical baseline that can be used to examine the impact of process changes, formulation, and storage conditions on the tertiary structure and stability of protein and viral antigens 21, 43.

Differential scanning calorimetry (DSC) is an experimental method that directly measures the difference in heat energy uptake taking place in a sample relative to a reference during a regulated temperature change 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12. Carried out in a differential scanning calorimeter, the method involves introducing heat energy into a sample cell and a reference cell simultaneously while identically increasing the temperature of both cells over time 2, 13, 14. Due to difference in the composition of the sample and the reference, different amount of energy will be required to raise the temperature of the cells 2, 12, 13. Thus, the excess amount of energy required to compensate for the temperature difference between the cells is measured and directly correlated to specific thermodynamic properties of the sample 1, 3.
In the 1960s, M.J. O&#39;Neil and E. Watson of Perkin Elmer developed the first differential scanning calorimeter to measure the heat flow of solid materials 2, 3, 4. In parallel, P.L. Privalov and D.R. Monaseldze E.L. of the Institute of Physics, Republic of Georgia (former USSR) created a unique differential adiabatic calorimeter that can be used for biochemical research 5, 6. Subsequently, Andronikashvili&#39;s team at the Institute of Physics, Republic of Georgia, reported the heat capacity of biomolecules such as fibrous and globular proteins, DNA, and RNA using DSC 7, 8, 9. Several teams led by Sturtevant 10, 11, 12, Brandts 13, and Privalov 14, 15, 16focused on the development of the theory and practical applications of DSC to investigate the thermodynamic details of protein unfolding. The value of DSC in studying large supramolecular structures such as phages, chloroplast, phospholipid liquid crystals, and meat proteins have also been reported 17, 18, 19, 20.
DSC has now become commonplace in pharmaceutical research and development for the assessment of the thermal stability of biomolecules, especially proteins 1, 21, 22. This is mostly due to advancements in terms of sensitivity and automation of the instrumentation used to carry out the experiment 23, 24. Here, the final result of the DSC experiment, namely, molar heat capacity as a function of temperature, is used to estimate&#160; the following thermodynamic parameters (change in heat capacity (&#8710;Cp), enthalpy (&#8710;H), entropy (&#8710;S), and Gibbs free energy (&#8710;G)) using the equation below:
<img alt="Equation 1" src="/files/ftp_upload/55262/55262eq1.jpg" /> &#160; (1)
<img alt="Equation 2" src="/files/ftp_upload/55262/55262eq2.jpg" />&#160;&#160; (2)
<img alt="Equation 3" src="/files/ftp_upload/55262/55262eq3.jpg" />&#160;&#160; (3)
<img alt="Equation 4" src="/files/ftp_upload/55262/55262eq4.jpg" /> &#160; (4)
<img alt="Equation 5" src="/files/ftp_upload/55262/55262eq5.jpg" />&#160;&#160; (5)
Where Cp is measured heat capacity; q is the heat flow into the test material; T0 and T are the initial and final temperatures of the transition respectively 22, 25. It is also worth noting that the equations above apply to single-domain proteins that can undergo two-state transition and reversible thermal unfolding 22. Analysis of more complex proteins (e.g., non-two-state proteins, and oligomers) have been reported by Friere et al. 26; Johnson et al. 27; and Kasimova et al. 28.
To determine whether a protein undergoes two-state transition or forms intermediates during thermal denaturation, the experimentally derived enthalpy (&#916;H; also referred to as calorimetric enthalpy &#916;HCal) is compared to the enthalpy derived using the van&#39;t Hoff equation given below (also referred to as van&#39;t Hoff enthalpy; &#916;HVH):
<img alt="Equation 6" src="/files/ftp_upload/55262/55262eq6.jpg" /> &#160; (6)
Where Tm is the midpoint temperature of the transition, R is the ideal gas constant (1.987 cal mol-1 K-1) and Y is the fraction of the protein population in the unfolded state 16, 29. If &#916;HVH is equal to &#916;HCal; or &#916;HVH/&#916;HCal is equal to 1, then the protein undergoes an &#34;all-or-none&#34; transition (i.e. two-state transition) 16, 25, 29. However, if &#916;HVH is less than &#916;HCal; or &#916;HVH/&#916;HCal is less than 1, the protein undergoes a non-two-state transition 16, 25, 29. The ratio of &#916;HVH/&#916;HCal also corresponds to the proportion of the protein structure that melts as a thermodynamic cooperative unit or domain 26.
The thermodynamic parameters mentioned above such as &#916;G and &#916;H provide useful information about the thermal stability of proteins, including biologics 30. However, emphasis will be laid on Tm and &#916;H in this publication, as they are the reported values for this protocol. Tm is the mid-point temperature of the transition, where the folded and the unfolded states of the protein are at equilibrium (i.e., &#916;G = 0) 25, 31. The higher the Tm of a protein, the higher its thermal stability 31. &#916;H corresponds to the area under the peak(s) of the Heat Capacity versus Temperature graph (also known as thermogram) generated at the end of the DSC experiment 16, 25. It is the energy required to denature proteins and can be used to estimate the active fraction (Fa) in a protein formulation (i.e., the proportion of proteins with active conformation in a sample) using the following equation:
<img alt="Equation 7" src="/files/ftp_upload/55262/55262eq7.jpg" /> &#160; (7)
Where &#916;H is the experimentally derived enthalpy of the protein sample and Q is the enthalpy determined for a well characterized reference or standardized protein 22. The estimation of Fa is significant for monitoring the real-time stability of products as well as conducting stability studies under stress conditions as required by ICH guidelines 32. Comparison of &#916;H also provides information about the compactness of the tertiary structure conformation of a protein 31.
This protocol details a procedure for assessing the thermal stability of proteins in an industrial setting and has been extensively used for the formulation of vaccines. It was developed using an automated differential scanning calorimeter that generates reproducible results for protein concentrations as low as 300 &#181;g/mL.

<table border="0" cellpadding="0" cellspacing="0" width="1375">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:331px;">Differential Scanning Calorimeter</td>
			<td style="width:188px;">Malvern Instruments Ltd</td>
			<td style="width:119px;">28428948 (Via GE Healthcare)</td>
			<td style="width:739px;">Has an autosampler for automated dispensing of samples into the cell to reduce human effort and errors.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Contrad 100</td>
			<td style="width:188px;">Decon Laboratories Inc</td>
			<td align="right" style="width:119px;">1504</td>
			<td>Dilute with water to 20% before use</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">500&#181;L Polypropylene round bottom 96 well plate</td>
			<td>Canadian Life Science</td>
			<td>ML072100</td>
			<td>Equivalent plates from other suppliers (e.g., VW) can also be used</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">MicroCal ThermoVac&#160;</td>
			<td style="width:188px;">Malvern Instruments Ltd</td>
			<td style="width:119px;">N/Ap</td>
			<td>provided with the Cap VP DSC</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Biosafety cabinet&#160;</td>
			<td style="width:188px;">Labconco</td>
			<td style="width:119px;">Logic+ - A2</td>
			<td>biocontainment laminar flow cabinet for sample preparation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Slide-A-Lyzer dialysis cassette</td>
			<td style="width:188px;">Thermo Scientific</td>
			<td style="width:119px;">66810 or 66380</td>
			<td>to equilibrate the sample and buffer</td>
		</tr>
	</tbody>
</table>

differential scanning calorimetry &#8212; a method for assessing the thermal stability and conformation of protein antigen

Differential scanning calorimetry (DSC) is an analytical technique that measures the molar heat capacity of samples as a function of temperature. In the case of protein samples, DSC profiles provide information about thermal stability, and to some extent serves as a structural &#8220;fingerprint&#8221; that can be used to assess structural conformation. It is performed using a differential scanning calorimeter that measures the thermal transition temperature (melting temperature; Tm) and the energy required to disrupt the interactions stabilizing the tertiary structure (enthalpy; &#8710;H) of proteins. Comparisons are made between formulations as well as production lots, and differences in derived values indicate differences in thermal stability and structural conformation. Data illustrating the use of DSC in an industrial setting for stability studies as well as monitoring key manufacturing steps are provided as proof of the effectiveness of this protocol. In comparison to other methods for assessing the thermal stability of protein conformations, DSC is cost-effective, requires few sample preparation steps, and also provides a complete thermodynamic profile of the protein unfolding process.

The raw data from most DSC experiments are presented as a heat flux versus temperature graph, as the calorimeter actually measures the difference in the rate of heat flow into the sample solution and buffer 35. Therefore, if both cells (i.e. sample and reference cells) contain identical solutions during an experiment, the raw data from the scan should be a flat line with no observable peaks. Any peak observed can be attributed to instrumentation error (e.g. damaged or contaminated cells), which is why running buffer scans prior to sample analysis is an adequate system suitability test. Figure 1 illustrates the result of a typical buffer scan indicating that the calorimeter was in good working condition prior to sample analysis.
Figure 2 shows the raw data for a DSC experiment carried out on different lots of two protein samples. As implied earlier, the observed peaks are the differences in the heat flux of the samples and their respective buffers. Differences in sample concentration can cause variations in heat capacity recorded by the calorimeter; however, these variations are normalized during sample analysis as per section 4.2 of the procedure. Higher concentrations can also reveal additional thermodynamic domains not contributing to the transition at lower concentrations. In addition, each transition represents a thermodynamic domain that may include one or more structural domains of the protein 36. In this case, the Protein 1 has three structural domains that melt cooperatively.
Figure 3 shows the results generated from the analysis of the raw data for protein 1 and 2 presented in Figure 2, i.e., after baseline subtraction and iterative curve fitting. The resulting thermograms have been normalized for scanning rate (automatically carried out by a pre-set algorithm in the analysis software) and concentration; thus, presenting the results of the experiment in comparable heat capacity versus temperature graphs. The analysis software uses data from the Heat Capacity versus Temperature graphs, such as Tm and &#916;Cp, to derive other thermodynamic parameters using variations of the equations given above depending on the cooperativity of protein unfolding.
When testing unknown samples, setting the appropriate temperature range is crucial. Otherwise, incomplete thermograms may result, as illustrated in Figure 4. Although Tm from such profiles can be derived, &#916;H cannot be accurately determined. Therefore, the sample must be retested with a larger temperature range to completely capture the thermal transition. Some proteins also readily form aggregates after complete denaturation, resulting in an increasing post-transition heat capacity: this often appears as an incomplete thermogram as illustrated in Figure 2B. However, retesting with a higher final temperature can help confirm whether there is an occurrence of a conformational transition at that region of the thermogram or it is merely the heat absorbing effect of protein aggregates.
Thermal stability is one of the most significant physical properties of proteins and protein-based products in the industry 37. In pharmaceuticals, it is used to determine the stability of biologics under different conditions, including formulation buffers and environmental factors such as humidity and temperature. It is also used to monitor key manufacturing step (e.g., purification and detoxification) to ensure conformational consistency between production lots. Figures 5 and 6 illustrate the use of DSC to examine the effects of chemical detoxification and storage conditions respectively on the stability and structural conformation of two different proteins. The significant differences in Tm and &#8710;H indicate conformational changes and protein degradation respectively. In addition, the loss of the third transition in Figure 6 further illustrates the degradation of a domain which was confirmed by a decrease in molecular weight when the samples were analyzed using Size-Exclusion Chromatography with multi-angle light scattering (SEC-MALS) (data not shown).
<img alt="Figure 1" src="/files/ftp_upload/55262/55262fig1.jpg" /> 
Figure 1: Buffer Scans. The similarity in gradient of each scan with no observable peaks indicates that the instrument is in good working condition and generated reproducible results. <a href="http://cloudflare.jove.com/files/ftp_upload/55262/55262fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55262/55262fig2.jpg" /> 
Figure 2: Raw Data Collected from DSC Experiments. These graphs are good representations of the unanalyzed (raw) data acquired after experimental runs (i.e. prior to baseline subtraction and curve fitting). Each line represents a production lot. Protein 2 tends to aggregate more readily upon heating, resulting in an increase in heat capacity above 100 &#176;C in the post-transition region of the thermogram. <a href="http://cloudflare.jove.com/files/ftp_upload/55262/55262fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55262/55262fig3.jpg" /> 
Figure 3: Analyzed DSC Data. These graphs are good representations of analyzed DSC data (i.e. after baseline subtraction and curve fitting). The blue line represents the thermogram after baseline subtraction, while the red line represents the curve with the best fit to the thermogram. (A) The Tm and &#916;H for Protein 1 Sample # 12 are 80.16 &#176;C and 1.69 x 106 cal/mol respectively. (B) The Tm and &#916;H for Protein 1 Sample# 13 are 80.15 &#176;C and 1.71 x 106 cal/mol respectively. (C) The Tm and &#916;H for Protein 2 Sample # 21 are 75.01 &#176;C and 4.08 x 106 cal/mol respectively. (D) The Tm and &#916;H for Protein 2 Sample # 22 are 75.67 &#176;C and 4.22 x 106 cal/mol respectively. <a href="http://cloudflare.jove.com/files/ftp_upload/55262/55262fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/55262/55262fig4.jpg" /> 
Figure 4: An Incomplete Thermogram. Raw data collect for Protein 1 analyzed at an inadequate temperature range. The final temperature of experiment was set to 90 &#176;C which did not accommodate the entire transition profile of the protein as compared to the experiment for Figure 2A which was set to 120 &#176;C. <a href="http://cloudflare.jove.com/files/ftp_upload/55262/55262fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/55262/55262fig5.jpg" /> 
Figure 5: Analyzed Data Showing the Effect of Chemical Detoxification on the Tertiary Structure of Protein 1. (A) Protein 1 is a toxin in its native conformation and has its Tm at 56.84 &#176;C and &#916;H at 2.57 x 105 cal/mol. (B) The detoxified form of Protein 1 (i.e. toxoid) has Tm and &#916;H values of 81.01 &#176;C and 1.89 x 106 cal/mol respectively. Thus, it can be concluded that the detoxification step introduced some form of variation to the structural conformation of Protein 1 which confers greater stability (higher Tm) to its detoxified form. <a href="http://cloudflare.jove.com/files/ftp_upload/55262/55262fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/55262/55262fig6.jpg" /> 
Figure 6: Analyzed Data Showing the Effect of Storage Conditions on the Conformation of Protein 3. These graphs illustrate the effect of storage temperature (2 - 8 &#176;C) on the stability and tertiary structure of Protein 3 over 30 weeks. The Tm and &#916;H values for Protein 3 at the 8th (A) and 38th week (B) of storage are given in Table 1 below. <a href="http://cloudflare.jove.com/files/ftp_upload/55262/55262fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Sample</td>
			<td>Tm 1 (&#176;C)</td>
			<td>&#8710;H 1 (cal/mol)</td>
			<td>Tm 2 (&#176;C)</td>
			<td>&#8710;H 2 (cal/mol)</td>
			<td>Tm 3 (&#176;C)</td>
			<td>&#8710;H 3 (cal/mol)</td>
		</tr>
		<tr>
			<td>Protein 3 stored at 2-8 &#176;C for 8 weeks</td>
			<td>61.91</td>
			<td>1.71 x 105</td>
			<td>75.54</td>
			<td>2.17 x 105</td>
			<td>90.29</td>
			<td>4.17 x 105</td>
		</tr>
		<tr>
			<td>Protein 3 stored at 2-8 &#176;C for 38 weeks</td>
			<td>61.87</td>
			<td>1.66 x 105</td>
			<td>75.18</td>
			<td>1.46 x 105</td>
			<td>90.22</td>
			<td>5.76 x 105</td>
		</tr>
	</tbody>
</table>
Table 1: Tm and &#916;H Values for Protein 3 at the 8th and 38th Week of storage at 2 - 8 &#176;C. Although the Tm values at both time points are similar, the difference in the &#916;H values indicates that the tertiary structure of Protein 3 has degraded over 30 weeks under the specified storage condition.

Watch this Scientific Journal Video about Differential Scanning Calorimetry Ã¢â‚¬â€ A Method for Assessing the Thermal Stability and Conformation of Protein Antigen at JoVE.com

Differential Scanning Calorimetry &#8212; A Method for Assessing the Thermal Stability and Conformation of Protein Antigen

Differential scanning calorimetry measures the thermal transition temperature(s) and total heat energy required to denature a protein. Results obtained are used to assess the thermal stability of protein antigens in vaccine formulations.

Differential scanning calorimetry (DSC) is an analytical technique that measures the molar heat capacity of samples as a function of temperature. In the ...

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38.5K Views.  Sanofi Pasteur Limited. Differential scanning calorimetry measures the thermal transition temperature(s) and total heat energy required to denature a protein. Results obtained are used to assess the thermal stability of protein antigens in vaccine formulations.

Video: Differential Scanning Calorimetry — A Method for Assessing the Thermal Stability and Conformation of Protein Antigen

Method for Identifying Small Molecule Inhibitors of the Protein-protein Interaction Between HCN1 and TRIP8b

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the excitability of neurons. The subcellular distribution of these channels in pyramidal neurons of hippocampal area CA1 is regulated by tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b), an auxiliary subunit. Genetic knockout of HCN pore forming subunits or TRIP8b, both lead to an increase in antidepressant-like behavior, suggesting that limiting the function of HCN channels may be useful as a treatment for Major Depressive Disorder (MDD). Despite significant therapeutic interest, HCN channels are also expressed in the heart, where they regulate rhythmicity. To circumvent off-target issues associated with blocking cardiac HCN channels, our lab has recently proposed targeting the protein-protein interaction between HCN and TRIP8b in order to specifically disrupt HCN channel function in the brain. TRIP8b binds to HCN pore forming subunits at two distinct interaction sites, although here the focus is on the interaction between the tetratricopeptide repeat (TPR) domains of TRIP8b and the C terminal tail of HCN1. In this protocol, an expanded description of a method for purifying TRIP8b and executing a high throughput screen to identify small molecule inhibitors of the interaction between HCN and TRIP8b, is described. The method for high throughput screening utilizes a Fluorescence Polarization (FP) -based assay to monitor the binding of a large TRIP8b fragment to a fluorophore-tagged eleven amino acid peptide corresponding to the HCN1 C terminal tail. This method allows &#39;hit&#39; compounds to be identified based on the change in the polarization of emitted light. Validation assays are then performed to ensure that &#39;hit&#39; compounds are not artifactual.

The interaction between HCN channels and their auxiliary subunit has been identified as a therapeutic target in Major Depressive Disorder. Here, a fluorescence polarization-based method for identifying small molecule inhibitors of this protein-protein interaction, is presented.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the ...

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 ...

Using Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating these studies, reincorporation of detergent-solubilized membrane proteins into liposomes is a stochastic process where protein topology is impossible to enforce. This paper offers an alternative method to these challenging techniques that utilizes a liposome-based scaffold. Protein solubility is enhanced by deletion of the transmembrane domain, and these amino acids are replaced with a tethering moiety, such as a His-tag. This tether interacts with an anchoring group (Ni2+ coordinated by nitrilotriacetic acid (NTA(Ni2+)) for His-tagged proteins), which enforces a uniform protein topology at the surface of the liposome. An example is presented wherein the interaction between Dynamin-related protein 1 (Drp1) with an integral membrane protein, Mitochondrial Fission Factor (Mff), was investigated using this scaffold liposome method. In this work, we have demonstrated the ability of Mff to efficiently recruit soluble Drp1 to the surface of liposomes, which stimulated its GTPase activity. Moreover, Drp1 was able to tubulate the Mff-decorated lipid template in the presence of specific lipids. This example demonstrates the effectiveness of scaffold liposomes using structural and functional assays and highlights the role of Mff in regulating Drp1 activity.

Using Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

This paper describes a method for assessing the interactions and assemblies of integral membrane proteins in vitro with various partner factors in a lipid-proximal environment.

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating ...

A Protein Preparation Method for the High-throughput Identification of Proteins Interacting with a Nuclear Cofactor Using LC-MS/MS Analysis

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in regulating transcription. These include the direct interaction with transcription factors, the covalent modification of histones and other proteins, and the occasional chromatin conformation alteration. Accordingly, establishing relatively quick methods for identifying proteins that interact within this network is crucial to enhancing our understanding of the underlying regulatory mechanisms. LC-MS/MS-mediated protein binding partner identification is a validated technique used to analyze protein-protein interactions. By immunoprecipitating a previously-identified member of a protein complex with an antibody (occasionally with an antibody for a tagged protein), it is possible to identify its unknown protein interactions via mass spectrometry analysis. Here, we present a method of protein preparation for the LC-MS/MS-mediated high-throughput identification of protein interactions involving nuclear cofactors and their binding partners. This method allows for a better understanding of the transcriptional regulatory mechanisms of the targeted nuclear factors.

We have established a method for the purification of coregulatory interaction proteins using the LC-MS/MS system.

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in ...

Multimer-PAGE: A Method for Capturing and Resolving Protein Complexes in Biological Samples

There are many well-developed methods for purifying and studying single proteins and peptides. However, most cellular functions are carried out by networks of interacting protein complexes, which are often difficult to investigate because their binding is non-covalent and easily perturbed by purification techniques. This work describes a method of stabilizing and separating native protein complexes from unmodified tissue using two-dimensional polyacrylamide gel electrophoresis. Tissue lysate is loaded onto a non-denaturing blue-native polyacrylamide gel, then an electric current is applied until the protein migrates a short distance into the gel. The gel strip containing the migrated protein is then excised and incubated with the amine-reactive cross-linking reagent dithiobis(succinimidyl propionate), which covalently stabilizes protein complexes. The gel strip containing cross-linked complexes is then cast into a sodium dodecyl sulfate polyacrylamide gel, and the complexes are separated completely. The method relies on techniques and materials familiar to most molecular biologists, meaning it is inexpensive and easy to learn. While it is limited in its ability to adequately separate extremely large complexes, and has not been universally successful, the method was able to capture a wide variety of well-studied complexes, and is likely applicable to many systems of interest.

A method for stabilizing and separating native protein complexes from unmodified tissue lysate using an amine-reactive protein cross-linker coupled to a novel two-dimensional polyacrylamide gel electrophoresis (PAGE) system is presented.

There are many well-developed methods for purifying and studying single proteins and peptides. However, most cellular functions are carried out by ...

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. Hydrogen-Deuterium Exchange (HDX) measured at rapid time scales is uniquely suited to detect structures and hydrogen bonding networks that are briefly populated, allowing for the characterization of transient conformers in native ensembles. Coupling of HDX to mass spectrometry offers several key advantages, including high sensitivity, low sample consumption and no restriction on protein size. This technique has advanced greatly in the last several decades, including the ability to monitor HDX labeling times on the millisecond time scale. In addition, by incorporating the HDX workflow onto a microfluidic platform housing an acidic protease microreactor, we are able to localize dynamic properties at the peptide level. In this study, Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) coupled to HDX was used to provide a detailed picture of residual structure in the tau protein, as well as the conformational shifts induced upon hyperphosphorylation.

Conformational flexibility plays a critical role in protein function. Herein, we describe the use of time-resolved electrospray ionization mass spectrometry coupled to hydrogen-deuterium exchange for probing the rapid structural changes that drive function in ordered and disordered proteins.

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. ...

A Colorimetric Method for Measuring Iron Content in Plants

Iron, one of the most important micronutrients in living organisms, is involved in basic processes, such as respiration and photosynthesis. Iron content is rather low in all organisms, amounting in plants to about 0.009% of dry weight. To date, one of the most accurate methods for measuring iron concentration in plant tissues is flame absorption atomic spectroscopy. However, this approach is time-consuming and expensive and requires specific equipment not commonly found in plant laboratories. Therefore, a simpler, yet accurate method that can be routinely used is needed. The colorimetric Prussian Blue method is regularly used for qualitative iron staining in animal and plant histological sections. In this study, we adapted the Prussian Blue method for quantitative measurements of iron in tobacco leaves. We validated the accuracy of this method using both atomic spectroscopy and Prussian Blue staining to measure iron content in the same samples and found a linear regression (R2 = 0.988) between the two procedures. We conclude that the Prussian Blue method for quantitative iron measurement in plant tissues is precise, simple, and inexpensive. However, the linear regression presented here may not be appropriate for other plant species, due to potential interactions between the sample and the reagent. Establishment of a regression curve is thus needed for different plant species.

We present a simple and reliable protocol for measuring iron content in plant tissues using the colorimetric Prussian Blue method.

Iron, one of the most important micronutrients in living organisms, is involved in basic processes, such as respiration and photosynthesis. Iron content ...

Assessing the Viability of a Synthetic Bacterial Consortium on the In Vitro Gut Host-microbe Interface

The interplay between host and microbiota has been long recognized and extensively described. The mouth is similar to other sections of the gastrointestinal tract, as resident microbiota occurs and prevents colonisation by exogenous bacteria. Indeed, more than 600 species of bacteria are found in the oral cavity, and a single individual may carry around 100 different at any time. Oral bacteria possess the ability to adhere to the various niches in the oral ecosystem, thus becoming integrated within the resident microbial communities, and favouring growth and survival. However, the flow of bacteria into the gut during swallowing has been proposed to disturb the balance of the gut microbiota. In fact, oral administration of P. gingivalis shifted bacterial composition in the ileal microflora. We used a synthetic community as a simplified representation of the natural oral ecosystem, to elucidate the survival and viability of oral bacteria subjected to simulated gastrointestinal transit conditions. Fourteen species were selected, subjected to in vitro salivary, gastric, and intestinal digestion processes, and presented to a multicompartment cell model containing Caco-2 and HT29-MTX cells to simulate the gut mucosal epithelium. This model served to unravel the impact of swallowed bacteria on cells involved in the enterohepatic circulation. Using synthetic communities allows for controllability and reproducibility. Thus, this methodology can be adapted to assess pathogen viability and subsequent inflammation-associated changes, colonization capacity of probiotic mixtures, and ultimately, potential bacterial impact on the presystemic circulation.

Assessing the Viability of a Synthetic Bacterial Consortium on the In Vitro Gut Host-microbe Interface

Gut host-microbe interactions were assessed using a novel approach combining a synthetic oral community, in vitro gastrointestinal digestion, and a model of the small intestine epithelium. We present a method that can be adapted to evaluate cell invasion of pathogens and multi-species biofilms, or even to test probiotic formulations&#39; survivability.

The interplay between host and microbiota has been long recognized and extensively described. The mouth is similar to other sections of the ...

How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project

The Horizon2020 Virus-X project was established in 2015 to explore the virosphere of selected extreme biotopes and discover novel viral proteins. To evaluate the potential biotechnical value of these proteins, the analysis of protein structures and functions is a central challenge in this program. The stability of protein sample is essential to provide meaningful assay results and increase the crystallizability of the targets. The thermal shift assay (TSA), a fluorescence-based technique, is established as a popular method for optimizing the conditions for protein stability in high-throughput. In TSAs, the employed fluorophores are extrinsic, environmentally-sensitive dyes. An alternative, similar technique is nano differential scanning fluorimetry (nanoDSF), which relies on protein native fluorescence. We present here a novel osmolyte screen, a 96-condition screen of organic additives designed to guide crystallization trials through preliminary TSA experiments. Together with previously-developed pH and salt screens, the set of three screens provides a comprehensive analysis of protein stability in a wide range of buffer systems and additives. The utility of the screens is demonstrated in the TSA and nanoDSF analysis of lysozyme and Protein X, a target protein of the Virus-X project.

A protocol is presented to rapidly test the thermal stability of proteins in a variety of conditions through thermal shift assays and nano differential scanning fluorimetry. Buffer systems, salts and additives, together comprising three unique stability screens, are assayed with proteins to identify suitable buffers for functional and structural studies.

The Horizon2020 Virus-X project was established in 2015 to explore the virosphere of selected extreme biotopes and discover novel viral proteins. To ...

Human Peripheral Blood Neutrophil Isolation for Interrogating the Parkinson's Associated LRRK2 Kinase Pathway by Assessing Rab10 Phosphorylation

The leucine rich repeat kinase 2 (LRRK2) is the most frequently mutated gene in hereditary Parkinson&#8217; disease (PD) and all pathogenic LRRK2 mutations result in hyperactivation of its kinase function. Here, we describe an easy and robust assay to quantify LRRK2 kinase pathway activity in human peripheral blood neutrophils by measuring LRRK2-controlled phosphorylation of one of its physiological substrates, Rab10 at threonine 73. The immunoblotting analysis described requires a fully selective and phosphospecific antibody that recognizes the Rab10 Thr73 epitope phosphorylated by LRRK2, such as the MJFF-pRab10 rabbit monoclonal antibody. It uses human peripheral blood neutrophils, because peripheral blood is easily accessible and neutrophils are an abundant and homogenous constituent. Importantly, neutrophils express relatively high levels of both LRRK2 and Rab10. A potential drawback of neutrophils is their high intrinsic serine protease activity, which necessitates the use of very potent protease inhibitors such as the organophosphorus neurotoxin diisopropylfluorophosphate (DIFP) as part of the lysis buffer. Nevertheless, neutrophils are a valuable resource for research into LRRK2 kinase pathway activity in vivo and should be considered for inclusion into PD biorepository collections.

Mutations in the leucine rich repeat kinase 2 gene (LRRK2) cause hereditary Parkinson&#8217;s disease. We have developed an easy and robust method for assessing LRRK2-controlled phosphorylation of Rab10 in human peripheral blood neutrophils. This may help identify individuals with increased LRRK2 kinase pathway activity.