A.K. was supported by a Japan Society for Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C) (#18K06201), by a grant-in-aid from the Nakatani Foundation for Countermeasures against novel coronavirus infections, by a grant from Hokkaido University Office for Developing Future Research Leaders (L-Station), and a grant-in-aid from Hoansha Foundation. M. K. was partially supported by a JSPS Grant-in-Aid for Scientific Research on Innovative Areas &#34;Chemistry for Multimolecular Crowding Biosystems&#34; (#20H04686), and a JSPS Grant-in-Aid for Scientific Research on Innovative Areas &#34;Information physics of living matters&#34; (#20H05522).

1. Materials and reagents
<ol>
	<li>Use pyrogenic free solutions and medium for cell culture (Table 1).</li>
	<li>Prepare solutions for the biochemical experiment using ultrapure water and use as DNase/RNase free.</li>
	<li>Select an appropriate FBS for the cell culture with a lot check process. Since the selected FBS lot changes regularly, the catalog and lot number for FBS cannot be represented here.</li>
	<li>Plasmid DNA
	<ol>
		<li>Prepare pmeGFP-N15 for eGFP monomer expression; pmeGFP-C1-TDP256 for GFP-TDP25 expression; pmeGFP-N1-SOD1-G85R7 for SOD1-G85R-GFP expression; and pCAGGS8 as a carrier. 
		NOTE: meGFP is a monomeric variant carrying A206K mutation of enhanced GFP (eGFP). TDP25 is an ALS-associated C-terminal fragment of TDP-43. SOD1-G85R is an ALS-associated mutant of SOD1.</li>
	</ol>
	</li>
	<li>Cell strain
	<ol>
		<li>Use murine neuroblastoma Neuro2a cells. 
		&#8203;NOTE: Neuro2a cells have high transfection efficiency and highly express exogenous proteins. For TDP25 expression, cell strains having high expression efficiencies such as Neuro2a or HEK293 cells are required. In HeLa cells, TDP25 was not able to be efficiently expressed.</li>
	</ol>
	</li>
</ol>
2. Cell culture and transfection
<ol>
	<li>Prepare a 100 mm plastic dish growing Neuro2a cells semi-confluently in normal growth medium. 
	NOTE: It is necessary to incubate at 37 &#176;C for approximately 48-72 hours after the previous seeding until semi-confluency is reached.</li>
	<li>Remove the medium.</li>
	<li>Add 0.5 mL of trypsin-EDTA solution into the cell-culture dish and incubate them at 37 &#176;C for 1 min.</li>
	<li>Add 9.5 mL of normal growth medium into the dish and suspend the detached cells.</li>
	<li>Using Trypan blue to stain the dead cells, count the number of cells using a cell counter or manually. Then dilute the cells in culture medium (1.0 &#215; 105/mL).</li>
	<li>Add 2 mL of cell suspension into a 35 mm plastic dish for cell lysis or a glass base dish for live-cell measurement.</li>
	<li>Incubate the dish at 37 &#176;C for 1 day.</li>
	<li>Start the following preparations 15 minutes before the day of the transfection.</li>
	<li>Prepare two 1.5 mL tubes and add 100 &#956;L of Opti-MEM I to each tube.</li>
	<li>In the first tube, mix 1.0 &#956;g of plasmid DNA (Solution A). In the second tube, mix 2.5 &#956;L of Lipofectamine 2000 (Solution B). 
	NOTE: To maintain transfection efficiency, keep the total DNA amount the same. To reduce the expression level for FCS measurement in live cells, the fraction amount of plasmid DNA for protein expression should decrease (e.g., 0.2 &#956;g of pmeGFP-N1-SOD1-G85R and 0.8 &#956;g of pCAGGS mixture). Moreover, keep the same ratio between the volume of Lipofectamine 2000 and the amount of plasmid DNA.</li>
	<li>Gently mix Solution A and B by adding one into either tube, and then incubate it for 1 min at room temperature (Solution C).</li>
	<li>Add the Solution C to the culture medium; and incubate the cells for 24 h at 37 &#176;C.</li>
</ol>
3. Cell lysis &#38; medium exchange
<ol>
	<li>Check the GFP expression using a routine microscope.</li>
	<li>Remove the medium in the dish.</li>
	<li>Add 2 mL of PBS at 25 &#176;C to wash out the medium. Remove the PBS.</li>
	<li>Place the dish on an aluminum plate on top of the crushed ice. 
	NOTE: We use a 1 mm thick aluminum plate cut in a Sunday tool store or commercially available one as laboratory equipment.</li>
	<li>Add 200 &#956;L of lysis buffer at 4 &#176;C. Shake the dish mildly so that the buffer is evenly distributed at the bottom of the dish.</li>
	<li>Scrape the dish using a cell scraper and recover the lysate with undissolved cell debris in a new 1.5 mL tube.</li>
	<li>Centrifuge the solution at 20,400 x g for 5 minutes at 4 &#176;C.</li>
	<li>Recover the supernatant in a new 1.5 mL tube and keep it at 4 &#176;C or on ice. 
	NOTE: Do not freeze the lysate.</li>
	<li>For live cell measurements, replace the medium before the measurement. Check the cell attachment using a phase-contrast microscope. Confirm expression using a fluorescence microscope.</li>
</ol>
4. Fluorescence correlation spectroscopy (FCS) calibration
<ol>
	<li>Start the system and run the operation software. Turn on the Argon+ laser (458/477/488/514 nm). Stabilize the system at least for 30 min.</li>
	<li>Set up the optical path: Main beam splitter: HFT488; Dichroic mirror: NFT600; Fluorescence barrier filter: a bandpass filter 505-540 (BP505-540); Detector: avalanche photodiode (APD)).</li>
	<li>Set the pinhole size by entering the value directly (66 &#956;m; 1 airy unit).</li>
	<li>Add the Rhodamine 6G (Rh6G) solution into a well of the cover glass chamber on the stage.</li>
	<li>Add the immersion ultrapure water on the objective. Do not use the immersion oil.</li>
	<li>Set the the chamber on the microscope stage. Move the stage to the appropriate position.</li>
	<li>Focus on the upper glass surface by measuring the scattered light from the glass surface. After lowering the objective lens to the bottom, turn the focus knob clockwise to adjust. 
	NOTE: Check the turning direction of the focus knob because it is opposite between those made in Japan and Germany.</li>
	<li>Raise the focal point 200 &#956;m above the upper glass surface to observe the inside of the solution.</li>
	<li>Click the Count rate and monitor the photon counts rate.</li>
	<li>Gradually increase the acousto-optic tunable filters (AOTF) value (e.g., excitation laser transmission) so that the count rate value is more than 10 kHz.</li>
	<li>Click the Pinhole Adjustment and open the pinhole adjustment wizard. Find the pinhole position with the highest (a peak) number of photons in both the x- and y-axis.</li>
	<li>Turn the correction ring of the objective lens so that the counts per molecule (CPM) value is the highest. 
	NOTE: Since the thickness of the cover glass of the chamber specified here is 0.12-0.17 mm, the correction ring is at around its minimum or just a few turns from it, where the CPM is at its maximum.</li>
	<li>Decrease the AOTF value gradually so that the CPM value becomes 2-3 kHz.</li>
	<li>Acquire the autocorrelation function (ACF) of Rh6G for 90 s.</li>
	<li>Once measurement is complete, click Fit to perform curve fitting analysis.</li>
	<li>Select a model for one-component three-dimensional (3D) diffusion with a triplet state. 
	NOTE: The Rh6G is monodisperse and one-component diffuse with a triplet state.</li>
	<li>Set the fitting start time by moving the red line. Click Fit All and check the fit deviation. After performing the curve fitting, make sure the diffusion time (DT) and the structure parameter (SP) are roughly in the range of 20-30 &#956;s and 4-8, respectively.</li>
	<li>Remember the structural parameter value. Use the SP value for curve fitting analysis of all ACFs measured on the same day, under the same optical conditions, and using the same type of glass base dish or chambered cover glass set on the microscope stage.</li>
</ol>
5. FCS measurement in cell lysate
<ol>
	<li>Prepare the cell lysates (see above).</li>
	<li>Place the lysate on the cover glass chamber. Place a lid to avoid drying and the stage lid for light shading.</li>
	<li>Set the acquisition condition, laser power, measurement time, and repetitions.</li>
	<li>Click Count rate and adjust the AOTF value of the laser so that the CPM value becomes more than 1 kHz.</li>
	<li>Perform a trial measurement for 1 min. Check whether the calculated ACF shows a positive amplitude and smooth decay.</li>
	<li>Perform the main measurement for 5 min. 
	NOTE: The total measurement time is gradually increased until the shape of the ACF does not change even if the measurement time is increased.</li>
	<li>Click Fit to perform curve fitting analysis.</li>
	<li>Select a model for two-component 3D diffusion with a triplet state. Remember to enter the SP value and change its setting to &#34;Fixed&#34; not &#34;Free&#34; before clicking Fit all.</li>
	<li>Export the fitted table as a tab-delimited text file.</li>
	<li>If necessary, export the ACF and Count rate record as a tab-delimited text file.</li>
</ol>
6. FCS measurement in live cells
<ol>
	<li>Replace the medium with a fresh one before the measurement.</li>
	<li>Use a heat stage incubator. Set the cell-culture dish on the microscope stage.</li>
	<li>Confirm the focus and position using the confocal micrope. Select the measuring cell. Zoom in and adjust the cell position. Acquire fluorescence images of a GFP-expressing cell using the slow scanning speed mode.</li>
	<li>Select an FCS-measuring position using Position in the &#34;FCS&#34; section.</li>
	<li>Select at least one FCS measurement point using a crosshair (Figure 1).</li>
	<li>Measure the ACF for 1 min. 
	NOTE: Because fluorescent proteins tend to be photobleached in live cells due to slow movement compared to the solution, the measurement time should be minimum. It is generally difficult to obtain ACF in the cell as smoothly as solution measurements because prolonged measurement will lead to photobleaching of fluorescent proteins.</li>
	<li>Click Fit to perform curve fitting analysis using a model for two-component 3D diffusion with a triplet state. 
	NOTE: For live cell measurements, a model for two-component 3D diffusion with a triplet state would be better because it is empirically difficult to decrease the chi-square value with a one-component 3D diffusion model because of the existence of various mobile components in the live cell. But even using a model for three-component 3D diffusion, the diffusion components are usually not separated compared to using the model for two-components.</li>
</ol>

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These authors have no conflicts of interest.

Regarding system calibration before measurements, the same glasswares as the one used to measure the sample should be used (e.g., the 8-wells cover glass chamber for cell lysate and the 35-mm glass base dish for live cells). Because of the adsorption of Rh6G on the glass, its effective concentration may sometimes decrease. If so, a highly concentrated Rh6G solution such as 1 &#956;M should be used just for the pinhole adjustment. Extremely high photon count rates must be avoided to protect the detector (e.g., more than 1000 kHz). Moreover, the concentrated solution is not appropriate for the acquisition of autocorrelation function (maintain less than 100 kHz). The calibration of pinhole position is important. If the optical system is used frequently, it is rare for dramatical dislocation of the pinhole; thus, using &#34;Fine&#34; at the beginning is often no problem to find the appropriate position. If no peak position of the count rates using &#34;Fine&#34; is found, use the &#34;Coarse&#34; to move the pinhole from one end of the range of motion to the other before the position-finding using &#34;Fine&#34;. If the amplitude of the ACF was flat, check whether the focus and position are appropriate.
After the Rh6G measurement and its fitting, if the DT and/or SP is out of the indicated range, re-try the pinhole and correction ring adjustment. When still showing out of the range, we recommend contacting the manufacturer&#39;s support as it is likely due to suspected defection of the optical system. Using the measured DT and SP in addition to the known diffusion coefficient of Rh6G (414 &#956;m2/s) in water, the effective beam waist can be calculated because DT is dependent on the beam waist10. Moreover, as the SP is the ratio of beam waist and the height of the effective detection volume, the detection volume can be calculated. The volume calculation is required to determine absolute concentration. More description of the principle and troubleshooting during the calibration is also available in protocols as reported previously11,12,13.
Some spikes were observed in the FCS measurement of GFP-TDP25 in cell lysate (Figure 2A, top). We show that such spikes were observed in FCS measurement of aggregate-prone huntingtin including expanded polyglutamine repeats labeled with GFP or yellow fluorescent protein (YFP) (HttQ78 and HttQ143)9; it thus suggests soluble oligomers/aggregates of GFP-TDP25 in the soluble fraction of the cell lysate. But such spike population was rare; thus, the ACF and its curve-fitting result may not include a major contribution of such spikes. A possible reason for only a small amount of aggregates being included in the soluble fraction is likely because TDP25 is highly aggregation-prone and its aggregates are fractionated in the insoluble fraction as shown using a fractionation of the cell lysate followed by western blotting detection6. The phenomena of the tendency to recover to the insoluble fraction of aggregation-prone protein have been often observed6,9. Such soluble oligomers/aggregates cannot be easily detected using conventional biochemical methods such as SDS-PAGE followed by western blotting; thus, FCS has an advantage. However, to analyze multi-components using conventional FCS is difficult because it measures the average population. More developmental procedures combined with Bayesian nonparametric analysis would determine the multi-components in the sample without any assumptions for the components14.
The diffusion time in live cells was relatively slow compared to the cell lysate. Since viscosity in the cell is known to be higher than that in solutions such as PBS and detergent-containing buffer15, the diffusion time in live cells theoretically gets 2.5-3 times longer. Due to this slow diffusion in live cells comparing in solution, photobleaching of fluorescent tags can often be caused. To correct the photobleaching effect on ACFs, some procedures such as exponential decay assumption16 and noise filtering using wavelet function have been proposed17. Although such corrections are believed to be effective, it has still not been so simple for general users because it requires programming skills. Moreover, in live cell measurements, the fitting range should be determined more carefully so that the chi-square value of the fitted curve becomes small. As shown in Figure 2B, the range where G(&#964;) was less than 1 was excluded from the fitting range. Alternatively, more photostable fluorescent tags are required to analyze slowly diffusing/moving molecules. GFP is easy to use as a labeling tag, and its stability is well, but it is often photobleached during FCS measurements. To overcome this photostable property, HaloTag with tetramethylrhodamine (TMR) as a chemical fluorescent dye has been available18. However, incomplete labeling by the fluorescent ligands (e.g., TMR-ligand) and trapped dye pools are problematic issues for specific labeling of proteins of interest19; thus, exogenous expression of protein-of-interest tagged with fluorescent proteins would be the first choice for fluorescent labeling in live cells.
There are many kinds of fluorescent proteins, as well as chemical fluorescent dyes; however, as shown in this article, monomeric enhanced GFP (meGFP) is an easy-to-use tag for FCS as well as other fluorescence microscopy because its biochemical and fluorescence properties are well known. Therefore, in our studies, meGFP is the first choice and generally used to label the aggregation-prone proteins for FCS measurements6,7,9.

Protein aggregations involving neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease, Parkinson's disease, Huntington's disease, and so on1 are known to be toxic and would disturb protein homeostasis (proteostasis) in the cells and organs, that could then lead to aging2. The clearance of protein aggregation is expected as a therapeutic strategy; however, chemicals that prevent protein aggregation formation and degrade protein aggregates (e.g., small molecules or drugs) have not been established yet. Moreover, how protein aggregation exerts toxicity remains elusive. Therefore, to promote research projects related to protein aggregation, it is important to introduce high throughput procedures to simply detect protein aggregation. Protein aggregation detection using antibodies recognizing the conformation of protein aggregation and aggregation-specific fluorescent dye has been widely used3. However, it is difficult to detect the aggregation, especially in live cells using such classical procedures. 
F&#246;rster resonance energy transfer (FRET) is a procedure to detect protein aggregation and structural change. However, FRET is unable to analyze protein dynamics (e.g., diffusion and oligomerization of protein in live cells)3. Therefore, we introduce here a simple protocol to detect protein aggregation in solution (e.g., cell lysate) and live cells using fluorescence correlation spectroscopy (FCS), which measures the diffusion property and brightness of fluorescent molecules with single molecule sensitivity4. FCS is a photon-counting method by using a laser scan confocal microscope (LSM). Using a highly sensitive photon-detector and calculation of autocorrelation function (ACF) of photon arrival time, the pass through time and brightness of the fluorescent molecules in the detection volume is measured. The diffusion slows with an increase of molecular weight; thus, intermolecular interaction can be estimated using FCS. Even more powerfully, an increase in the brightness of the fluorescent molecule indicates homo-oligomerization of the molecules. Therefore, FCS is a powerful tool to detect such protein aggregation.

<table><tbody><tr><td>0.25% (w/v) Trypsin-1 mmol/L EDTA&amp;middot;4Na Solution with Phenol Red (Trypsin-EDTA)</td><td>Fujifilm Wako Pure Chemical Corp.</td><td>201-16945</td><td></td></tr><tr><td>100-mm plastic dishes</td><td>CORNING</td><td>430167</td><td></td></tr><tr><td>35-mm glass base dish</td><td>IWAKI</td><td>3910-035</td><td>For live cell measurement</td></tr><tr><td>35-mm plastic dishes</td><td>Thermo Fisher Scientific</td><td>150460</td><td></td></tr><tr><td>Aluminum plate</td><td>Bio-Bik</td><td>AB-TC1</td><td></td></tr><tr><td>C-Apochromat 40x/1.2NA Korr. UV-VIS-IR M27</td><td>Carl Zeiss</td><td></td><td>Objective</td></tr><tr><td>Cell scraper</td><td>Sumitomo Bakelite Co., Ltd.</td><td>MS-93100</td><td></td></tr><tr><td>Cellulose acetate filter membrane (0.22 mm)</td><td>Advantech Toyo</td><td>25CS020AS</td><td></td></tr><tr><td>Cover glass chamber 8-wells</td><td>IWAKI</td><td>5232-008</td><td>For solution measurement</td></tr><tr><td>Dulbecco&#039;s Modified Eagle&#039;s Medium (DMEM)</td><td>Sigma-Aldrich</td><td>D5796</td><td>basal medium</td></tr><tr><td>Fetal bovine serum (FBS)</td><td>biosera</td><td></td><td>Lot check required</td></tr><tr><td>Lipofectamine 2000</td><td>Thermo Fisher Scientific</td><td>11668019</td><td></td></tr><tr><td>LSM510 META + ConfoCor3</td><td>Carl Zeiss</td><td></td><td>FCS system</td></tr><tr><td>Murine neuroblastoma Neuro2a cells</td><td>ATCC</td><td>CCL-131</td><td>Cell line</td></tr><tr><td>Opti-MEM I</td><td>Thermo Fisher Scientific</td><td>31985070</td><td></td></tr><tr><td>pCAGGS</td><td>RIKEN</td><td>RDB08938</td><td>Plasmid DNA for the transfection carrier</td></tr><tr><td>Penicillin-Streptomycin Solution (&amp;times;100 )</td><td>Fujifilm Wako Pure Chemical Corp.</td><td>168-23191</td><td></td></tr><tr><td>pmeGFP-C1-TDP25</td><td></td><td></td><td>Plasmid DNA for TDP25 tagged with monomeric eGFP</td></tr><tr><td>pmeGFP-N1</td><td></td><td></td><td>Plasmid DNA for eGFP monomer expression</td></tr><tr><td>pmeGFP-N1-SOD1-G85R</td><td></td><td></td><td>Plasmid DNA for ALS-linked G85R mutant of SOD1 tagged with monomeric eGFP</td></tr><tr><td>Protease inhibitor cocktail</td><td>Sigma-Aldrich</td><td>P8304</td><td></td></tr></tbody></table>

detection of protein aggregation using fluorescence correlation spectroscopy

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and so on. To detect and analyze soluble or diffuse protein oligomers or aggregates, fluorescence correlation spectroscopy (FCS), which can detect the diffusion speed and brightness of a single particle with a single molecule sensitivity, has been used. However, the proper procedure and know-how for protein aggregation detection have not been widely shared. Here, we show a standard procedure of FCS measurement for diffusion properties of aggregation-prone proteins in cell lysate and live cells: ALS-associated 25 kDa carboxyl-terminal fragment of TAR DNA/RNA-binding protein 43 kDa (TDP25) and superoxide dismutase 1 (SOD1). The representative results show that a part of aggregates of green fluorescent protein (GFP)-tagged TDP25 was slightly included in the soluble fraction of murine neuroblastoma Neuro2a cell lysate. Moreover, GFP-tagged SOD1 carrying ALS-associated mutation shows a slower diffusion in live cells. Accordingly, we here introduce the procedure to detect the protein aggregation via its diffusion property using FCS.

We performed FCS measurement of GFP-TDP25 in cell lysate and SOD1-G85R-GFP in live cells. In both cases, a positive amplitude and smooth ACFs were able to be acquired. We have shown that a portion of GFP-TDP25 expressed in Neuro2a cells was recovered in the soluble fraction under the indicated condition6. In the soluble fraction of the cell lysate, extremely bright fluorescence molecules were detected in the photon count rate record using FCS (Figure 2A, top, arrow). Such &#34;spikes (also called burst)&#34; were not observed in GFP monomers and monodisperse chemical fluorescent dye solution, suggesting that the spikes indicate oligomeric proteins9. Curve fitting analysis using a model assuming two-component 3D diffusion with a triplet state showed that the fast diffusing molecules (DTFast = 186 &#956;s) were ~90% and the remaining 10% was 2.3 ms (Figure 2A, bottom; and Table 2).
During the SOD1-G85R-GFP measurement in live cells, the photon count rate record showed a gradual decrease, suggesting its photobleaching in the detection volume (Figure 2B, top). Although the contribution of the photobleaching was shown in a longer than 1 s range in the ACF, a positive amplitude and smooth decay of the ACF were observed (Figure 2B, bottom). Curve fitting analysis using a model assuming two-component 3D diffusion with a triplet state showed that the fast diffusing molecules (DTFast = 397 &#956;s) were ~93.4% and the remaining 6.6% was 12.3 ms (Table 2). ALS-linked mutation, G85R, in SOD1 did not allow a dramatic difference in diffusion property compared to wild type. However, proteasome inhibition decreased the diffusion rate only in the G85R mutant of SOD1 in the cytoplasm7.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62576/62576fig01.jpg" /> 
Figure 1: Confocal fluorescent image of Neuro2a cells expressing SOD1-G85R-GFP.&#160; Confocal fluorescent image of Neuro2a cells expressing SOD1-G85R-GFP. The crosshair indicates the FCS measurement position in the cytoplasm. Bar = 10 &#956;m.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62576/62576fig01large.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/62576/62576fig02.jpg" /> 
Figure 2: Typical FCS results and fitted curves for the autocorrelation functions.&#160;&#160;(A and B) Top: Recorded photon count rate in the FCS measurement time range (Green line). Bottom: Calculated autocorrelation functions (ACFs; Raw, grey lines) and fitted ACF curves using a model for two-component three-dimensional diffusion with a triplet state (Fit, magenta lines). G(&#964;) for Y-axis indicates the amplitude of ACFs at time &#964; sec. for X-axis. Dot lines show fitting start and end time points. Dark blue arrows show the spike with extremely bright proteins passing through the detection volume (i.e., soluble oligomers/aggregates). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62576/62576fig02large.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>Name of Material/Equipment</td>
			<td>Components</td>
			<td>Comments/Description</td>
		</tr>
		<tr>
			<td>0.1 mM Rhodamine 6G solution.</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>1 M HEPES-KOH pH 7.5</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>2 M Sodium chloride (NaCl)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Lysis buffer</td>
			<td>50 mM HEPES-KOH pH 7.5, 150 mM NaCl, 1% Triton X-100, 1&#215; protease inhibitor cocktail</td>
			<td>Protease inhibitor cocktail should be mixed just before the cell lysis.</td>
		</tr>
		<tr>
			<td>Normal growth medium</td>
			<td>DMEM supplemented with 10% FBS and 100 U/mL penicillin G and 100 mg/mL Streptomycin</td>
			<td>Lot check for FBS should be required.</td>
		</tr>
		<tr>
			<td>Phosphate buffer saline (PBS)</td>
			<td>137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: Solution compositions
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>CPM (kHz)</td>
			<td>DTFast (ms)</td>
			<td>Fast component (%)</td>
			<td>DTSlow (ms)</td>
			<td>Slow component (%)</td>
		</tr>
		<tr>
			<td>GFP-TDP25 in lysate</td>
			<td>7.9</td>
			<td>186</td>
			<td>89.7</td>
			<td>2.3</td>
			<td>10.3</td>
		</tr>
		<tr>
			<td>SOD1-G85R-GFP in live cell</td>
			<td>6.3</td>
			<td>397</td>
			<td>93.4</td>
			<td>12.3</td>
			<td>6.6</td>
		</tr>
	</tbody>
</table>
Table 2: Typical fitted values for the autocorrelation functions 
Fitted values for the autocorrelation functions are represented in Figure 2 using a model for two-component three-dimensional diffusion with a triplet state. Counts per molecule (CPM), fast and slow diffusion time (DTFast and DTSlow, respectively), and their components (Fast and Slow component) are represented.

Watch this Scientific Journal Video about Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy at JoVE.com

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

We here introduce a procedure to measure protein oligomers and aggregation in cell lysate and live cells using fluorescence correlation spectroscopy.

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's ...

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Protein Structure

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5.5K Views.  Hokkaido University. Het beste principe van de fluorescentiecorrelatiespectroscopie, FCS, werd aanvankelijk uitgevonden in de jaren 1970. In de jaren 1990 werden belangrijke en vele verbeteringen voor FCS vastgesteld door te combineren met een confocale apparaatmicroscopie. Sindsdien wordt FCS gebruikt voor vele chemische en viruskuurtoepassingen, zoals moleculaire interacties en eiwitaggregatieanalyse.Eiwitaggregatie is een kenmerk van neurodegeneratieve aandoeningen. Fluorescentiehelderheid zijn enkelvou...

Video: Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring cells. Of interest, only few assays are capable of specifically probing such interactions directly in living cells. Here, we present an assay to measure the binding of proteins expressed at the surfaces of neighboring cells, at cell-cell contacts. This assay consists of two steps: mixing of cells expressing the proteins of interest fused to different fluorescent proteins, followed by fluorescence fluctuation spectroscopy measurements at cell-cell contacts using a confocal laser scanning microscope. We demonstrate the feasibility of this assay in a biologically relevant context by measuring the interactions of the amyloid precursor-like protein 1 (APLP1) across cell-cell junctions. We provide detailed protocols on the data acquisition using fluorescence-based techniques (scanning fluorescence cross-correlation spectroscopy, cross-correlation number and brightness analysis) and the required instrument calibrations. Further, we discuss critical steps in the data analysis and how to identify and correct external, spurious signal variations, such as those due to photobleaching or cell movement.
In general, the presented assay is applicable to any homo- or heterotypic protein-protein interaction at cell-cell contacts, between cells of the same or different types and can be implemented on a commercial confocal laser scanning microscope. An important requirement is the stability of the system, which needs to be sufficient to probe diffusive dynamics of the proteins of interest over several minutes.

This protocol describes a fluorescence fluctuation spectroscopy-based approach to investigate interactions among proteins mediating cell-cell interactions, i.e. proteins localized in cell junctions, directly in living cells. We provide detailed guidelines on instrument calibration, data acquisition and analysis, including corrections to possible artefact sources.

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring ...

Biochemistry

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

The phenomenon of protein misfolding and aggregation results in the formation of highly heterogeneous protein aggregates, which are associated with neurodegenerative conditions such as Alzheimer&#8217;s and Parkinson&#8217;s diseases. In particular low molecular weight aggregates, amyloid oligomers, have been shown to possess generic cytotoxic properties and are implicated as neurotoxins in many forms of dementia. We illustrate the use of methods based on atomic force microscopy (AFM) to address the challenging task of characterizing the morphological, structural and chemical properties of these aggregates, which are difficult to study using conventional structural methods or bulk biophysical methods because of their heterogeneity and transient nature. Scanning probe microscopy approaches are now capable of investigating the morphology of amyloid aggregates with sub-nanometer resolution. We show here that infrared (IR) nanospectroscopy (AFM-IR), which simultaneously exploits the high resolution of AFM and the chemical recognition power of IR spectroscopy, can go further and enable the characterization of the structural properties of individual protein aggregates, and thus offer insights into the aggregation mechanisms. Since the approach that we describe can be applied also to the investigations of the interactions of protein assemblies with small molecules and antibodies, it can deliver fundamental information to develop new therapeutic compounds to diagnose or treat neurodegenerative disorders.

We describe the application of infrared nanospectroscopy and high-resolution atomic force microscopy to visualize the process of protein self-assembly into oligomeric aggregates and amyloid fibrils, which is closely associated with the onset and development of a wide range of human neurodegenerative disorders.

The phenomenon of protein misfolding and aggregation results in the formation of highly heterogeneous protein aggregates, which are associated with ...

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One &#945;-Synuclein Monomer at a Time

Using spectroscopic rulers to track multiple conformations of single biomolecules and their dynamics have revolutionized the understanding of structural dynamics and its contributions to biology. While the FRET-based ruler reports on inter-dye distances in the 3-10 nm range, other spectroscopic techniques, such as protein-induced fluorescence enhancement (PIFE), report on the proximity between a dye and a protein surface in the shorter 0-3 nm range. Regardless of the method of choice, its use in measuring freely-diffusing biomolecules one at a time retrieves histograms of the experimental parameter yielding separate centrally-distributed sub-populations of biomolecules, where each sub-population represents either a single conformation that stayed unchanged within milliseconds, or multiple conformations that interconvert much faster than milliseconds, and hence an averaged-out sub-population. In single-molecule FRET, where the reported parameter in histograms is the inter-dye FRET efficiency, an intrinsically disordered protein, such as the &#945;-Synuclein monomer in buffer, was previously reported as exhibiting a single averaged-out sub-population of multiple conformations interconverting rapidly. While these past findings depend on the 3-10 nm range of the FRET-based ruler, we sought to put this protein to the test using single-molecule PIFE, where we track the fluorescence lifetime of site-specific sCy3-labeled &#945;-Synuclein proteins one at a time. Interestingly, using this shorter range spectroscopic proximity sensor, sCy3-labeled &#945;-Synuclein exhibits several lifetime sub-populations with distinctly different mean lifetimes that interconvert in 10-100 ms. These results show that while &#945;-Synuclein might be disordered globally, it nonetheless attains stable local structures. In summary, in this work we highlight the advantage of using different spectroscopic proximity sensors that track local or global structural changes one biomolecule at a time.

Time-resolved single-molecule protein-induced fluorescence enhancement is a useful fluorescence spectroscopic proximity sensor sensitive to local structural changes in proteins. Here we show it can be used to uncover stable local conformations in &#945;-Synuclein, which is otherwise known as globularly unstructured and unstable when measured using the longer range FRET ruler.

Using spectroscopic rulers to track multiple conformations of single biomolecules and their dynamics have revolutionized the understanding of structural ...

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

We present a protocol and workflow to perform live cell dual-color fluorescence cross-correlation spectroscopy (FCCS) combined with F&#246;rster Resonance Energy transfer (FRET) to study membrane receptor dynamics in live cells using modern fluorescence labeling techniques. In dual-color FCCS, where the fluctuations in fluorescence intensity represent the dynamic &#34;fingerprint&#34; of the respective fluorescent biomolecule, we can probe co-diffusion or binding of the receptors. FRET, with its high sensitivity to molecular distances, serves as a well-known &#34;nanoruler&#34; to monitor intramolecular changes. Taken together, conformational changes and key parameters such as local receptor concentrations and mobility constants become accessible in cellular settings.
Quantitative fluorescence approaches are challenging in cells due to high noise levels and the vulnerability of the sample. Here we show how to perform this experiment, including the calibration steps using dual-color labeled &#946;2-adrenergic receptor (&#946;2AR) labeled with eGFP and SNAP-tag-TAMRA. A step-by-step data analysis procedure is provided using open-source software and templates that are easy to customize.
Our guideline enables researchers to unravel molecular interactions of biomolecules in live cells in situ with high reliability despite the limited signal-to-noise levels in live cell experiments. The operational window of FRET and particularly FCCS at low concentrations allows quantitative analysis at near-physiological conditions.

We present an experimental protocol and data analysis workflow to perform live cell dual-color fluorescence cross correlation spectroscopy (FCCS) combined with F&#246;rster Resonance Energy transfer (FRET) to study membrane receptor dynamics in live cells using modern fluorescence labeling techniques.

We present a protocol and workflow to perform live cell dual-color fluorescence cross-correlation spectroscopy (FCCS) combined with F&#246;rster Resonance ...

4D Imaging of Protein Aggregation in Live Cells

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental conditions and an intracellular environment that is tightly packed, sticky, and hazardous to protein stability1. The ability to dynamically balance protein production, folding and degradation demands highly-specialized quality control machinery, whose absolute necessity is observed best when it malfunctions. Diseases such as ALS, Alzheimer's, Parkinson's, and certain forms of Cystic Fibrosis have a direct link to protein folding quality control components2, and therefore future therapeutic development requires a basic understanding of underlying processes. Our experimental challenge is to understand how cells integrate damage signals and mount responses that are tailored to diverse circumstances.
The primary reason why protein misfolding represents an existential threat to the cell is the propensity of incorrectly folded proteins to aggregate, thus causing a global perturbation of the crowded and delicate intracellular folding environment1. The folding health, or "proteostasis," of the cellular proteome is maintained, even under the duress of aging, stress and oxidative damage, by the coordinated action of different mechanistic units in an elaborate quality control system3,4. A specialized machinery of molecular chaperones can bind non-native polypeptides and promote their folding into the native state1, target them for degradation by the ubiquitin-proteasome system5, or direct them to protective aggregation inclusions6-9.
In eukaryotes, the cytosolic aggregation quality control load is partitioned between two compartments8-10: the juxtanuclear quality control compartment (JUNQ) and the insoluble protein deposit (IPOD) (Figure 1 - model). Proteins that are ubiquitinated by the protein folding quality control machinery are delivered to the JUNQ, where they are processed for degradation by the proteasome. Misfolded proteins that are not ubiquitinated are diverted to the IPOD, where they are actively aggregated in a protective compartment.
Up until this point, the methodological paradigm of live-cell fluorescence microscopy has largely been to label proteins and track their locations in the cell at specific time-points and usually in two dimensions. As new technologies have begun to grant experimenters unprecedented access to the submicron scale in living cells, the dynamic architecture of the cytosol has come into view as a challenging new frontier for experimental characterization. We present a method for rapidly monitoring the 3D spatial distributions of multiple fluorescently labeled proteins in the yeast cytosol over time. 3D timelapse (4D imaging) is not merely a technical challenge; rather, it also facilitates a dramatic shift in the conceptual framework used to analyze cellular structure.
We utilize a cytosolic folding sensor protein in live yeast to visualize distinct fates for misfolded proteins in cellular aggregation quality control, using rapid 4D fluorescent imaging. The temperature sensitive mutant of the Ubc9 protein10-12 (Ubc9ts) is extremely effective both as a sensor of cellular proteostasis, and a physiological model for tracking aggregation quality control. As with most ts proteins, Ubc9ts is fully folded and functional at permissive temperatures due to active cellular chaperones. Above 30 &deg;C, or when the cell faces misfolding stress, Ubc9ts misfolds and follows the fate of a native globular protein that has been misfolded due to mutation, heat denaturation, or oxidative damage. By fusing it to GFP or other fluorophores, it can be tracked in 3D as it forms Stress Foci, or is directed to JUNQ or IPOD.

Cellular viability depends on timely and efficient management of protein misfolding. Here we describe a method for visualizing the different potential fates of a misfolded protein: refolding, degradation, or sequestration in inclusions. We demonstrate the use of a folding sensor, Ubc9ts, for monitoring proteostasis and aggregation quality control in live cells using 4D microscopy.

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental ...

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Confocal microscopy provides an accessible methodology to capture sub-cellular interactions critical for the characterization and further development of pre-clinical agents labeled with fluorescent probes. With recent advancements in antibody based cytotoxic drug delivery systems, understanding the alterations induced by these agents within the realm of receptor aggregation and internalization is of critical importance. This protocol leverages the well-established methodology of fluorescent immunocytochemistry and the open source FIJI distribution of ImageJ, with its inbuilt autocorrelation and image mathematical functions, to perform spatial image correlation spectroscopy (ICS). This protocol quantitates the fluorescent intensity of labeled receptors as a function of the beam area of the confocal microscope. This provides a quantitative measure of the state of target molecule aggregation on the cell surface. This methodology is focused on the characterization of static cells with potential to expand into temporal investigations of receptor aggregation. This protocol presents an accessible methodology to provide quantification of clustering events occurring at the cell surface, utilizing well established techniques and non-specialized imaging apparatus.

Antibodies that bind to target receptors on the cell surface can confer conformation and clustering alterations. These dynamic changes have implications for characterizing drug development in target cells. This protocol utilizes confocal microscopy and image correlation spectroscopy through ImageJ/FIJI to quantify the extent of receptor clustering on the cell surface.

Confocal microscopy provides an accessible methodology to capture sub-cellular interactions critical for the characterization and further development of ...

Detecting and Characterizing Protein Self-Assembly In Vivo by Flow Cytometry

Protein self-assembly governs protein function and compartmentalizes cellular processes in space and time. Current methods to study it suffer from low-sensitivity, indirect read-outs, limited throughput, and/or population-level rather than single-cell resolution. We designed a flow cytometry-based single methodology that addresses all of these limitations: Distributed Amphifluoric FRET or DAmFRET. DAmFRET detects and quantifies protein self-assemblies by sensitized emission FRET in vivo, enables deployment across model systems-from yeast to human cells-and achieves sensitive, single-cell, high-throughput read-outs irrespective of protein localization or solubility.

This article describes a FRET-based flow cytometry protocol to quantify protein self-assembly in both S. cerevisiae and HEK293T cells.

Protein self-assembly governs protein function and compartmentalizes cellular processes in space and time. Current methods to study it suffer from ...

High-Sensitivity Detection of Compound-Protein Binding Using Fluorescence

Detection of CD40 Protein-Umbelliferone Interaction via Differential Scanning Fluorescence

The investigation of interactions between different molecules is a crucial aspect of understanding disease pathogenesis and screening for drug targets. Umbelliferone, an active ingredient in Tibetan medicine Vicatia thibetica, exhibits an immunomodulatory effect with an unknown mechanism. The CD40 protein is a key target in the immune response. Therefore, this study employs the principle of differential scanning fluorescence technology to analyze the interactions between CD40 protein and umbelliferone using fluorescent enzyme markers. Initially, the stability of the protein fluorescent orange dye was experimentally verified, and the optimal dilution ratio of 1:500 was determined. Subsequently, it was observed that the temperature melting (Tm) value of CD40 protein tended to decrease with an increase in concentration. Interestingly, the interaction between CD40 protein and umbelliferone was found to enhance the thermal stability of CD40 protein. This study represents the first attempt to detect the binding potential of small molecule compounds and proteins using fluorescence microplates and fluorescent dyes. The technique is characterized by high sensitivity and accuracy, promising advancements in the fields of protein stability, protein structure, and protein-ligand interactions, thus facilitating further research and exploration.

Author Spotlight: Streamlining PCR Methods for Enhanced Accessibility and Efficiency

This protocol describes a unique method for detecting the binding between compounds and protein molecules, offering the advantages of minimal protein sample loss and high data accuracy.

The investigation of interactions between different molecules is a crucial aspect of understanding disease pathogenesis and screening for drug targets. ...

Medicine

Protein Aggregate Formation Assay: A Method to Detect and Quantify Protein Aggregation in Cultured Cells upon Induction by Proteasome Inhibitor

Source: Lee, H. et al. <a href="https://www-jove-com.remotexs.ntu.edu.sg/v/56425">Assay Development for High Content Quantification of Sod1 Mutant Protein Aggregate Formation in Living Cells.</a> J. Vis. Exp. (2017)
In this video, we demonstrate a cell-based protein aggregation assay using proteasome inhibitors, which block proteasome activity, preventing misfolded, mutant proteins, fused to a fluorescent label, from undergoing ubiquitin-dependent proteasomal degradation, leading to their accumulation within the cell cytoplasm. The protein aggregates are then visualized and quantified by fluorescence microscopy.

Source: Lee, H. et al. Assay Development for High Content Quantification of Sod1 Mutant Protein Aggregate Formation in Living Cells. J. Vis. Exp. (2017)
...

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Fluorescence Fluctuation Spectroscopy to Study Protein Homo-Oligomerization

Source: Nolan, R., et al. Calibration-free In Vitro <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/58157">Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software.</a> J. Vis. Exp. (2018)
This video demonstrates the technique of fluorescence fluctuation spectroscopy (FFS) to study protein homo-oligomerization. Fluorescently-labeled proteins in a sample are dimerized using an agent when studying protein oligomerization using FFS. Using a confocal microscope, as the proteins move in and out of the small observation volume, the analysis of the fluctuations in the brightness of the fluorescent molecules is performed to determine the oligomeric state of the proteins.

Source: Nolan, R., et al. Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source ...

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

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