Name: an innovative method for exosome quantification and size measurement
Uploaded: 2015-01-17T14:00:00
Duration: 11 min 38 s
Description: Watch this Scientific Journal Video about An Innovative Method for Exosome Quantification and Size Measurement at JoVE.com

The authors would like to thank Christina Ball&#225;zs, Hug Aubin and J&#246;rn H&#252;lsmann for the critical reading of the manuscript and excellent editorial assistance. Moreover, the authors thank Gisela Mueller for the technical assistance. The authors thank Particle Metrix GmbH for providing the funds covering the publication costs.

NOTE: The experiments presented in this work have been approved by the institutional ethical board of the University of Dusseldorf.
1. Exosome Preparation
<ol>
	<li>Collect whole blood in 3 citrate tubes via venipuncture (total of 9 ml). Pour the blood into a 15 ml Falcon tube.</li>
	<li>Centrifuge the sample at 1,500 x g for 20 min at 4 &#176;C to initiate separation of cells from plasma. Transfer the supernatant to a new 15 ml falcon tube.</li>
	<li>Centrifuge the sample at 2,800 x g for 20 min at 4 &#176;C to remove all cells from plasma (cell-free plasma; CFP). Transfer the CFP to ultracentrifugation tubes, 1 ml per tube.</li>
	<li>Centrifuge at 100,000 x g for 90 min at 4 &#176;C to deplete exosomes. Remove 900 &#181;l of supernatant. Re-suspend the pellet in the remaining 100 &#181;l in the ultracentrifugation tube. Add 900 &#181;l PBS.</li>
	<li>Centrifuge again at 100,000 x g for 30 min at 4 &#176;C. Remove 900 &#181;l of supernatant. Re-suspend the pellet with the remaining 100 &#181;l.</li>
	<li>Transfer 5 to 20 &#181;l of re-suspension in 40 ml distilled water. Filter the suspension through a 450 nm filter to separate exosomes from larger particles. Use this final suspension for particle measurement.</li>
</ol>
2. Startup Procedure of the Particle Tracking Instrument
<ol>
	<li>Start the program by double clicking on the software-icon. Click on the various software tabs (&#8220;Cell Check&#8221;, &#8220;Measurement&#8220;, &#8220;Analysis&#8221;) to switch between them throughout the protocol.</li>
	<li>Follow the instructions on the screen for an automated implementation of the startup procedure. Select the boxes for both cell quality check and auto alignment. 
	NOTE: Either of these steps can be repeated separately if necessary by pressing button (A) or (B) on the &#8220;cell check&#8221; tab.</li>
	<li>Open the inlet and outlet port of the NTA instrument and inject 10 ml distilled water by syringe into the cell channel through the inlet port. Close the outlet port as the last amount of water is being injected. Place a beaker under the outlet port to collect waste solution. Ensure that the measurement cell is free of air bubbles and do not syringe air bubbles into the system. Close the inlet port immediately.</li>
	<li>Perform the cell quality check by clicking on &#8220;OK&#8221;. The software will display the cell quality results after a few seconds.</li>
	<li>If any particles are still visualized in the live-view screen of the software or if the result of the quality check is only good or poor, repeat step 2.3 until the remaining particles are removed from the measurement cell. Also repeat step 2.3 after each measurement to avoid accumulation of particles. If repeating the distilled water injection and cell check does not produce a &#8220;good&#8221; outcome, proceed to 2.9.</li>
	<li>Prepare a control suspension containing uniform 200 nm sized polystyrene particles to be used to align the foci of the laser and microscope. Add one drop of the suspension concentrate, provided by the instrument manufacturer, to 500 ml of distilled water to obtain the required concentration such that 600 &#177; 100 particles are displayed per screen in the live-view.</li>
	<li>Inject the alignment suspension into the NTA instrument as described in step 2.3. Press &#8220;OK&#8221; to start auto alignment, an automated routine through which the system will automatically find the optimum position of the two foci.</li>
	<li>When a prompt appears stating the system is now ready for experiments, click on OK to start the measurement.</li>
	<li>Occasionally, clean the cell channel manually between experiments by flushing the cell with a 30% solution of ethanol. Clean the channel whenever the software displays an automatic error report.</li>
</ol>
3. Measurement of Sample
<ol>
	<li>Flush the channel with distilled water prior to each sample measurement (as described in step 2.3).</li>
	<li>Inject the exosome suspension (prepared in section 1) in to the channel, as described in step 2.3.</li>
	<li>Adjust the following main parameters in the software as needed:
	<ol>
		<li>Sensitivity. In order to find the optimal sensitivity range, click on the button &#8220;Number of Particles vs. Sensitivity&#8221; to display a curve for measured particles per screen for different sensitivity levels. Chose a sensitivity level before the maximum slope of the curve. A higher sensitivity level allows visualization of more small particles but also increases issues related to background noise.</li>
		<li>Minimum Brightness. Chose a starting brightness of 20 for exosome measurement to use this function as a filter. Regulate this parameter up to blank out strongly light-scattering particles. Regulate it down to amplify weakly scattering artifacts. Vary brightness as needed throughout the experiment. 
		NOTE: Particles that light-scatter too strongly overlap and can be excluded from the analysis by mistake.</li>
		<li>Min and Max Size. Set a digital filter to eliminate pixel noise and unwanted scatter from oversized particles by regulating the minimum and maximum size. Use a range of 10 to 500 pixels for measurement of exosomes.</li>
		<li>Shutter. Adjust the period of time that the camera is open to 1/300 sec.</li>
	</ol>
	</li>
	<li>Live read out parameters:
	<ol>
		<li>Switch between a digital and an analog view modus at any point by clicking on the &#8220;Live-image&#8221; button.</li>
		<li>Throughout the experiment, monitor the scattering Intensity bar, which displays the saturation status of the sample as a color coded indicator. Do not analyze exosomes if the scattering bar is red. In such a case further dilute the sample to avoid this phenomenon. If the experimental condition prohibits a manipulation of the sample suspension, down-regulate the sensitivity or up-regulate the brightness in the software-settings to improve the measurement results. 
		NOTE: When samples with an extremely high concentration of particles are analyzed the scatter will fuse individual particles and they are counted as one single particle.</li>
		<li>Note the number of particles counted in the field of vision from the display.</li>
	</ol>
	</li>
	<li>Click on the &#8220;Measurement&#8221; tab.</li>
	<li>Choose settings in the &#8220;run options&#8221; array.
	<ol>
		<li>Before each acquisition, select &#8220;check movement&#8217; for a repeated check particle drift test. Perform the test at least once before starting the whole analysis. If the drift is higher than 20 &#181;m/sec, wait before continuing the measurement so that the sample stops flowing.</li>
		<li>Select the number of experiments (5) and the time delay between them (0).</li>
		<li>Perform a &#8220;sample check&#8217; if necessary. This test is part of the auto alignment in the start up procedure and can be repeated afterwards as needed.</li>
	</ol>
	</li>
	<li>Finally click on the &#8220;run video acquisition&#8221; button. In the new window, define the number of cycles (15) and the number of measurement positions (11). 
	NOTE: The laser head and the microscope can be moved to analyze the particle number at 11 different positions. Depending on the experimental demand, decide whether the experiment should be performed on one single position or on different positions.
	<ol>
		<li>Confirm the minimal time duration a particle has to be tracked to be counted as one individual particle by setting the video resolution (low). A lower resolution results in a shorter tracking duration.</li>
		<li>Select a file-name and click on &#8220;OK&#8221; to start measurement.</li>
	</ol>
	</li>
</ol>
4. Interpretation of the Results
<ol>
	<li>View the following results and parameters displayed after measurement on the &#8220;results&#8221; tab: (1) total number of particles traced, (2) average number of particles per position, and (3) particle concentration.
	<ol>
		<li>View the result average table for the numerical ratio of particle size (diameter, surface and volume) to the percentage of particle numbers, as well as values of mean and standard differentiation.</li>
		<li>Use the peak analysis table if more than one peak exists in the graphical analysis. This table shows the distribution of particles that are smaller than the first or respectively second peak.</li>
	</ol>
	</li>
	<li>View the graph calculated after the measurement to see a distribution of particles by size. Use the icons in the display mode and above the graph to change the settings.</li>
</ol>

<ol>
	<li>Thery, C., Amigorena, S., Raposo, G., Clayton, A., Bonifacino, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chapter+3.+Isolation+and+characterization+of+exosomes+from+cell+culture+supernatants+and+biological+fluids.+.">Chapter 3. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. .</a> Current protocols in cell biology / editorial board. (3.22), (2006).</li><li>Taylor, D. D., Zacharias, W., Gercel-Taylor, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exosome+isolation+for+proteomic+analyses+and+RNA+profiling.">Exosome isolation for proteomic analyses and RNA profiling.</a> Methods in molecular biology. 728, 235-246 (2011).</li><li>Lasser, C., Eldh, M., Lotvall, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+and+characterization+of+RNA-containing+exosomes.">Isolation and characterization of RNA-containing exosomes.</a> J. Vis. Exp. , e3037 (2012).</li><li>Konokhova, A. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Light-scattering+flow+cytometry+for+identification+and+characterization+of+blood+microparticles.">Light-scattering flow cytometry for identification and characterization of blood microparticles.</a> Journal of biomedical. 17, 057006 (2012).</li><li>Vlassov, A. V., Magdaleno, S., Setterquist, R., Conrad, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exosomes:+current+knowledge+of+their+composition,+biological+functions,+and+diagnostic+and+therapeutic+potentials.">Exosomes: current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials.</a> Biochimica et biophysica acta. 1820, 940-948 (2012).</li><li>Sokolova, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterisation+of+exosomes+derived+from+human+cells+by+nanoparticle+tracking+analysis+and+scanning+electron+microscopy.">Characterisation of exosomes derived from human cells by nanoparticle tracking analysis and scanning electron microscopy.</a> Colloids and surfaces. B, Biointerfaces. 87, 146-150 (2011).</li><li>Clayton, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+antigen+presenting+cell+derived+exosomes,+based+on+immuno-magnetic+isolation+and+flow+cytometry.">Analysis of antigen presenting cell derived exosomes, based on immuno-magnetic isolation and flow cytometry.</a> Journal of immunological. 247, 163-174 (2001).</li><li>Cheruvanky, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+isolation+of+urinary+exosomal+biomarkers+using+a+nanomembrane+ultrafiltration+concentrator.">Rapid isolation of urinary exosomal biomarkers using a nanomembrane ultrafiltration concentrator.</a> Amer. J. Phys. Renal Phys. 292, F1657-1661 (2007).</li><li>Carr, B., Warren, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Company+profile:+NanoSight:+delivering+practical+solutions+for+biological+nanotechnology.">Company profile: NanoSight: delivering practical solutions for biological nanotechnology.</a> Nanomedicine. 7, 1129-1132 (2012).</li><li>Caby, M. P., Lankar, D., Vincendeau-Scherrer, C., Raposo, G., Bonnerot, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exosomal-like+vesicles+are+present+in+human+blood+plasma.">Exosomal-like vesicles are present in human blood plasma.</a> International immunology. 17, 879-887 (2005).</li></ol>

This work was supported by the institutional funds of the Dept. of Cardiovascular Surgery, Medical Faculty, HHU. The publication costs of this study were provided by Particle MetrixGmbH.

We demonstrate a detailed protocol for exosome isolation from blood and present nanoparticle tracking as a novel and innovative method to measure exosome size and concentration in a biological fluid. In the presented experiments human peripheral blood was used as the origin of the exosomes. However, other origins, such as urine, sputum, cell culture supernatant, etc., may also be used as test material.
Based on the biological variability of exosome concentration in humans, assays from different individuals may feature a remarkably varying concentration of exosomes. However, the concentration of particles in the biological test probe may have an impact on the results. Therefore, a reliable and standardized approach for the dilution of probes is needed. In the presented method exosome containing plasma was generated from 9 ml of peripheral whole blood. Using differential centrifugation steps an exosome pellet was isolated from 1 ml plasma and re-suspended in 5 ml distilled water to result in the working sample of suspended exosomes. This pre-defined setting has provided us with a proper concentration of particles, i.e., appropriate scatter intensity during NTA. Beside the volume and dilution aspect, the composition of the solutions that are used is also of importance. We have used distilled water for the final re-suspension of exosomes. Of course, different media for the final dilution step may also be used, according to the requirements of the experimental set-up. However, in the case of zeta potential measurements of ionic-solutions, particularly when testing samples with a buffering capacity, a prudent dilution in turbulence free conditions is useful. For the direct comparison of multiple samples we recommend keeping the same dilution level for all samples. All settings except the sensitivity and the video resolution can be changed afterwards by using the ZetaView Analysis software.
Although the authors believe that the herein introduced system currently represents the most suitable instrument, it is not the only available NTA system. A number of previous reports have employed other systems that apply the same NTA principles but provide another instrument design that goes along with some differing features 10. We believe that practical aspects concerning the sample handling and reproducibility of the results are of central importance in choosing the methodological sequence for exosome evaluation. We also believe that an ideal detection system should eliminate user-depending factors that may interfere with the results of the measurements. The exosome analyzing approach that is presented here fulfills the criterion of easy handling to a high degree. As a further advantage, the semi-automated analysis of the samples is performed in a fast process, generating results in a short time. An online visualization of exosomes aids the analyzer to gain an instant idea of the exosome characteristics, e.g., gross concentration.
A very simple but elegant addition to the measurement technique presented here is the use of antibody labeled exosomes and the capture of the scattered laser beam by using a preceding filter in front of the detector. In this way exosome subpopulations may be distinguished according to their surface antigens and other biologically relevant characteristics that are technically accessible to a selective fluorescent staining.
One disadvantage of the current version of our particle tracking instrument is the fact that at very high sensitivity levels artifacts such as background noise may become present, which is based on the cell channel wall. A technical solution and improvement to the current system may become available in the near future. By this method reflections of the laser scatter light on the channel wall will be reduced, thereby increasing the accuracy of the measured signals and obtained data and lowering the limit of detection.
Although the currently used protocol for exosome isolation is well established and the applied particle tracking instrument has a remarkably precise resolution with a lower size distribution of 30 nm, there is no guarantee that the detected particles are indeed entirely true exosomes. Other particles, such as dead cell fragments or larger protein complexes may also be present in the exosome suspension and lead to false positive signals. One reliable method by which such &#8220;pollution&#8221; can be excluded may be electron microscopy (EM), either transmission EM or scanning EM. Unfortunately, no general and specific marker for exosomes has been identified so far, although a number of previously proposed surface markers have gained an increasing amount of attention, including tetraspanin (Tspan), CD81, C63, CD9, etc. 11.
Irrespective of the future progress of research on exosome biology, particularly concerning exosome specific markers, the protocol that is presented here will provide a powerful method for the isolation and detection of exosomes. Concentration and size may be readily determined in a software-assisted straightforward fashion. The addition of selective fluorescent markers or fluorophore coupled antibodies will likely further enhance the possible applications of the presented approach of NTA.

The exact function of circulating exosomes remained unknown for a long period of time. Even now the full path mechanism of exosomes is not fully understood. Since exosomes carry antigens, proteins and RNA (mRNA and miRNA) that relates them to their parental cell of origin, their function as cell-cell signaling transmitters has mainly been given priority.
Many different methods have been described in the literature for the isolation and quantitative detection of exosomes 1,2. However, no consensus on a &#8216;gold standard&#8217; has been reached. Meanwhile the majority of scientists active in the field of exosome research agree that a consistent method of isolation is highly warranted to achieve a higher degree of comparability between different reports and studies.
Fluorescence activated cell sorting (FACS) is the most common and prevalent tool for exosome analysis 3. FACS has the benefit that, via fluorescence labeling, cells from different origins can be compared in one step. The major disadvantages of FACS are that this method is not sensitive enough to identify particles smaller than 0.5 &#181;m 4, whereas exosomes are generally between 30-120 nm in diameter 5, even less to measure their size.
Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are other tools for the analysis of particle size and morphology of exosomes. However, both SEM and TEM have the disadvantage that the preparation of samples is time-consuming, both methods involve labor-intensive steps and each has some risk of artifact generation. Neither method is suitable for high sample throughput and characterization of several thousands of single particles of one sample. Moreover, a quantitative analysis for clinical daily routine where samples have often to be analyzed simultaneously or at least in a very short period is difficult to perform. New generation techniques now allow us to analyze exosomes without prior intensive preparatory work (e.g., environmental SEM). These modern techniques are still rather inconvenient for analyzing large volume suspensions containing exosomes to determine their average number and size distribution 6.
Another highly sensitive method for visualization and analysis of exosomes is nanoparticle-tracking analysis (NTA). This method takes advantage of two different principles of physics. First, particles are detected by the light scattered when they are irradiated with a laser beam. The second phenomenon is known as Brownian motion, according to which the diffusion of different particles in a liquid suspension is inversely proportional to their size. In the latter case the movement also depends on the temperature and the viscosity of the liquid. Yet, this rate is directly related to particle size and is used by NTA. Using software-based analysis, digital images of scattered light from single particles are recorded. Plots of scattered light spots and their speed of motion provide the data that facilitate the determination of total particle count and size distribution. This technique is particularly powerful for analyzing particles with a mean diameter of less than 100 nm.
The size and concentration measurements are performed with the ZetaView Brownian and Electrophoresis Motion Video Analysis Microscope. This is a semi-automated desk top nanoparticle analysis instrument for liquid samples (hereafter referred to as the particle tracking instrument). It consists of the particle tracking analyzer as well as a laptop with the software used for the data analysis. Heterogeneous biological samples are as suitable for this method as more homogeneous suspensions of inorganic particles. A laser scattering microscope with a video camera is used for the detection of particles and for the observation of their movement. While the microscope axis is horizontal and focused into the cell channel filled with a suspension containing exosomes, the laser beam is oriented vertically. The particles irradiated by the laser scatter the light, which is recorded under 90&#176; by a digital video camera via the microscope (Figure 1). The intensity of the scattered light allows observation of particles larger 60 nm diameter. In such a setting the brightness of a particle is not the only indication of particle size. When no electrical field is applied, particle movement only follows Brownian motion and may serve as an indicator for calculating particle size. However, the instrument is also capable of applying an electrical field across the cell channel. When subjected to this field, the potential, polarity and level of ionic-charge of the suspended exosomes become further determinants of the direction of their movement. Velocity and direction result in an electrophoretic mobility histogram.
While finding an optimal method to analyze isolated exosomes is one problem, another one lies in the effective isolation of exosomes from different media, such as blood, ascites, urine, milk, amniotic fluid or cell media. Different methods have been described thus far, which are based on ultracentrifugation 1, industrial separation reagents (such as Exoquick) 7, magnetic beads for antigen employing separation 8 or ultrafiltration steps 9.
In this protocol we demonstrate the entire process of exosome isolation via ultracentrifugation and show how to analyze the resulting exosome containing suspension via the particle tracking instrument. Specific considerations for the analysis of human plasma or cell culture medium derived exosomes are provided.

<table border="0" cellpadding="0" cellspacing="0" width="645">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:159px;">Name of the Reagent</td>
			<td style="width:116px;"></td>
			<td style="width:124px;"></td>
			<td style="width:247px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Citrate tube</td>
			<td>BD</td>
			<td>364305</td>
			<td>BD Vacutainer</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Distilled water</td>
			<td>Braun</td>
			<td>3880087</td>
			<td>Aqua ad iniectabilia</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Falcon tube</td>
			<td>Greiner Bio One</td>
			<td>188271</td>
			<td>PP Tube, Steril 15ml</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ultracentrifugation tube</td>
			<td>Beckman</td>
			<td>357448</td>
			<td>Microfuge Tube Polyallomer 1,5ml</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Polybead</td>
			<td>Polysciences, Inc.</td>
			<td>07304</td>
			<td>2,6% Solids-Latex 
			Alignment Solution</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Syringe (Filter)</td>
			<td>Braun</td>
			<td>4617053V</td>
			<td>5ml</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Syringe (ZetaView)</td>
			<td>Braun</td>
			<td>4606051V</td>
			<td>5ml</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Needle</td>
			<td>BD</td>
			<td>305180</td>
			<td>BD Blunt Fill Needle</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Filter</td>
			<td>Sartorius Stedim</td>
			<td>16555</td>
			<td>Syringe filter, hydrophilic, 450&#181;m</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;"></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;"></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Material Name&#160;</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ultracentrifuge</td>
			<td>Beckman</td>
			<td>L8-M</td>
			<td>Rotor: 70Ti Ser. No E21078</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ZetaView</td>
			<td>Particle Metrix</td>
			<td>PMX 100, Type 101</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Centrifuge</td>
			<td>Eppendorf</td>
			<td>5804R</td>
			<td>Rotor: A-4-44</td>
		</tr>
	</tbody>
</table>

an innovative method for exosome quantification and size measurement

Although the biological importance of exosomes has recently gained an increasing amount of scientific and clinical attention, much is still unknown about their complex pathways, their bioavailability and their diverse functions in health and disease. Current work focuses on the presence and the behavior of exosomes (in vitro as well as in vivo) in the context of different human disorders, especially in the fields of oncology, gynecology and cardiology.
Unfortunately, neither a consensus regarding a gold standard for exosome isolation exists, nor is there an agreement on such a method for their quantitative analysis. As there are many methods for the purification of exosomes and also many possibilities for their quantitative and qualitative analysis, it is difficult to determine a combination of methods for the ideal approach. 
Here, we demonstrate nanoparticle tracking analysis (NTA), a semi-automated method for the characterization of exosomes after isolation from human plasma by ultracentrifugation. The presented results show that this approach for isolation, as well as the determination of the average number and size of exosomes, delivers reproducible and valid data, as confirmed by other methods, such as scanning electron microscopy (SEM).

The sample used for this demonstration shows an optimum setting for measurement with a sensitivity of 85%, before the maximum slope of the sensitivity curve (Figure 2). The brightness, the min/max values were chosen as recommended in the protocol. A concentration of 5.3 particles/ml x 106 was measured, while the mean size of the particles was 0.149 &#181;m, most of them were 0.137 &#181;m.
The values received after measurement can be saved in a report with a file format of .pdf or as a .txt for exporting into a database. The graph can be adjusted as preferred as described in the protocol (section 4). The video sequence is also saved and can be used for later off-line re-analysis. However, in such an off-line analysis, the pre-acquisition settings of the camera cannot be changed retrospectively.
In order to find the optimum parameter settings for a measurement, we here describe the optimization of the instrument settings on the example of a 100 nm polystyrene size standard. The influence of two parameters, sensitivity and min/max size on video image and particle size distribution is discussed in detail. All other parameters are summarized in Table 1.
A visual impression of the impact of sensitivity (ranging from 50 to 94) on analog and digital images is visualized in Figure 3. The quantitative information derived from the images is presented in Figure 4, based on the settings in Table 1, Min Size = 5 and Max Size = 200. A typical relationship of the number of detected particles vs. sensitivity is shown in Figure 4A. Between 50 and 90, the number of detected particles increased with sensitivity and rose dramatically for sensitivities &#62;90. An optimum range of sensitivity was found between 66 and 86 (A). Particle size distributions obtained with different sensitivity settings are shown in Figure 4B. The particle size distributions represent the mean of three individual measurements. For too low a sensitivity (sensitivity = 62, red curve) only a few particles were analyzed resulting in rather poor statistics. The number of analyzed particles increased with sensitivity and reached an optimum between 70 (yellow curve) and 86 (tan curve). Further increasing the sensitivity lead to a deterioration of the particle size distribution with the number of particles dropping and size distribution shifting towards smaller sizes (sensitivity = 94, blue curve). Figure 4C shows the trend of the number based x50 diameter (50% of particles are smaller than this diameter) as a function of sensitivity. In the beige interval, the RSD of particle size was less than 8% and corresponds to the optimal interval in A. The red regions indicate RSD &#62; 8% as a result of poor statistics (sensitivity too low) or broad distributions with shift to smaller sizes (sensitivity too high).
The settings of Min Size and Max Size are filters applied to digital images in order to remove particles with spot sizes smaller than Min Size and larger than Max Size. Due to the ability to scatter light, a particle creates a spot of a certain size on a digital image. The size of the spot is measured as a number of pixels (px). When a particle scatters light very well (e.g., particles &#62; 200 nm or aggregates), the spot size is quite large, e.g., &#62; 500 px. The spot size is quite small (e.g.,&#160;&#60; 10 px) for small particles (e.g., &#60; 20 nm) depending on the particle material. Spot size (px) may not be interchanged with particle size (nm) as they are not identical and there is no direct relationship between these two variables. An optimization of Min and Max Size allows the user to filter out unwanted objects such as agglomerates (Max Size) or small objects such as background noise (Min Size). The influence of Min/Max Size on the particle size distribution of a 100 nm size standard is shown in Figure 4D (sensitivity = 82). When the interval is set to small spot sizes (e.g., min = 1, max = 52; orange curve), the number of analyzed particles is reduced and the number based x50 diameter is slightly shifted towards smaller sizes. A setting for larger spots (min = 40, max = 1,000; red curve) results in a broad particle size distribution shifted towards larger sizes. In order to obtain equal total numbers of particles, the interval boundaries of both orange and red distributions were adjusted to match 80 particles. The distribution with optimal settings (min = 5, max = 200; tan curve) consists of 360 particles.
A series of successful experiments were performed with exosomes isolated by ultracentrifugation and measured by NTA using the presented system. The resulting data were highly consistent and confirmed a high level of reproducibility. Other isolation methods should show similar results. However, the dilution step was identified as a particularly critical step and its impact on the calculated total numbers of particles has to be re-evaluated.
<img alt="Figure 1" src="/files/ftp_upload/50974/50974fig1.jpg" /> 
Figure 1. Schematic of the setup of the NTA. The microscope / video axis and laser beam are orientated orthogonally to each other, crossing at the cell channel cross section. Light scattered by the particles is displayed in the &#8220;live-view&#8221; window of the software. <a href="https://www.jove.com/files/ftp_upload/50974/50974fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/50974/50974fig2.jpg" /> 
Figure 2. Number of particles vs. sensitivity curve. The number of particles vs. sensitivity curve displays the particles in one position at one moment during an automatic sensitivity scan. This graphical visualization is used to determine the best preferences for the initial measurements. A sensitivity value is chosen prior to the maximum slope of the graph. It is important to remember that artifacts are not eliminated in this test experiment and can affect the graph. <a href="https://www.jove.com/files/ftp_upload/50974/50974fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/50974/50974fig3.jpg" /> 
Figure 3. Impact of sensitivity on analog and digital image. Visualization of particles on the live view screen is displayed for sensitivities between 50 and 94 for both analog (top row) and digital (bottom row) views. When the sensitivity is too low, only a few particles are detected (left). At optimum sensitivity the particles appear as single dots well isolated from each other (middle). At a comparatively high sensitivity particles merge together leading to poor image quality (right). <a href="https://www.jove.com/files/ftp_upload/50974/50974fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/50974/50974fig4.jpg" /> 
Figure 4. Influence of sensitivity min and max size settings for a control 100 nm polystyrene particle sample. (A) Plot of sensitivity versus detected number of particles; the optimal interval is from 66 to 86, prior to the maximum slope of the curve. (B) Particle size distributions obtained with several sensitivity settings (62 to 94); graphs for too low (62) or too high (94) sensitivity do not capture the particle size distribution for the 100 nm polystyrene control sample. (C) Number based x50 diameter versus sensitivity; the error of x50 in the beige interval is less than 8%, optimal interval is from 66 to 86. (D) Impact of Min and Max Size on the particle size distribution; optimal parameters (min = 5 and max = 200) capture the correct distribution for the control sample. <a href="https://www.jove.com/files/ftp_upload/50974/50974fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td colspan="2">Pre-acquisition parameters</td>
		</tr>
		<tr>
			<td>Sensitivity</td>
			<td>variable</td>
		</tr>
		<tr>
			<td>Shutter</td>
			<td>40</td>
		</tr>
		<tr>
			<td>Frame rate</td>
			<td>30 fps</td>
		</tr>
		<tr>
			<td>Resolution</td>
			<td>High</td>
		</tr>
		<tr>
			<td>Cycles</td>
			<td>10</td>
		</tr>
		<tr>
			<td>Multiple Acquisitions</td>
			<td>3</td>
		</tr>
		<tr>
			<td>Position</td>
			<td>1</td>
		</tr>
		<tr>
			<td colspan="2">Post-acquisition parameters</td>
		</tr>
		<tr>
			<td>Min Brightness</td>
			<td>30</td>
		</tr>
		<tr>
			<td>Max Size</td>
			<td>variable</td>
		</tr>
		<tr>
			<td>Min Size</td>
			<td>variable</td>
		</tr>
	</tbody>
</table>
Table 1. Summary of pre- and post-acquisition parameters for setting of the particle tracking instrument.

Watch this Scientific Journal Video about An Innovative Method for Exosome Quantification and Size Measurement at JoVE.com

An Innovative Method for Exosome Quantification and Size Measurement

A method for isolation of exosomes from whole blood and further analysis by nanoparticle tracking using a semi-automatic instrument is presented in this article. The presented technology provides an extremely sensitive method for visualizing and analyzing particles in liquid suspension.

Although the biological importance of exosomes has recently gained an increasing amount of scientific and clinical attention, much is still unknown about ...

an-innovative-method-for-exosome-quantification-and-size-measurement

Research

JoVE Journal

Biology

Method for Measurement of Viral Fusion Kinetics at the Single Particle Level

Membrane fusion is an essential step during entry of enveloped viruses into cells. Conventional fusion assays typically report on a large number of fusion events, making it difficult to quantitatively analyze the sequence of the molecular steps involved. We have developed an in vitro, two-color fluorescence assay to monitor kinetics of single virus particles fusing with a target bilayer on an essentially fluid support.
Influenza viral particles are incubated with a green lipophilic fluorophore to stain the membrane and a red hydrophilic fluorophore to stain the viral interior. We deposit a ganglioside-containing lipid bilayer on the dextran-functionilized glass surface of a flow cell, incubate the viral particles on the planar bilayer and image the fluorescence of a 100 x 100 &mu;m2 area, containing several hundreds of particles, on a CCD camera. By imaging both the red and green fluorescence, we can simultaneously monitor the behavior of the membrane dye (green) and the aqueous content (red) of the particles.
Upon lowering the pH to a value below the fusion pH, the particles will fuse with the membrane. Hemifusion, the merging of the outer leaflet of the viral membrane with the outer leaflet of the target membrane, will be visible as a sudden change in the green fluorescence of a particle. Upon the subsequent fusion of the two remaining distal leaflets a pore will be formed and the red-emitting fluorophore in the viral particle will be released under the target membrane. This event will give rise to a decrease of the red fluorescence of individual particles. Finally, the integrated fluorescence from a pH-sensitive fluorophore that is embedded in the target membrane reports on the exact time of the pH drop.
From the three fluorescence-time traces, all the important events (pH drop, lipid mixing upon hemifusion, content mixing upon pore formation) can now be extracted in a straightforward manner and for every particle individually. By collecting the elapsed times for the various transitions for many individual particles in histograms, we can determine the lifetimes of the corresponding intermediates. Even hidden intermediates that do not have a direct fluorescent observable can be visualized directly from these histograms.

We present an in vitro, two-color fluorescence assay to visualize the fusion of single virus particles with a fluid target bilayer. By labeling viral particles with fluorophores that differentially stain the viral membrane and its interior, we are able to monitor the kinetics of hemifusion and pore formation.

Membrane fusion is an essential step during entry of enveloped viruses into cells. Conventional fusion assays typically report on a large number of fusion ...

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), and delta-cells (somatostatin) that are embedded in the exocrine tissues and comprise 1-2% of the entire pancreas. There is a close correlation between body and pancreas weight. Total beta-cell mass also increases proportionately to compensate for the demand for insulin in the body. What escapes this proportionate expansion is the size distribution of islets. Large animals such as humans share similar islet size distributions with mice, suggesting that this micro-organ has a certain size limit to be functional. The inability of large animal pancreata to generate proportionately larger islets is compensated for by an increase in the number of islets and by an increase in the proportion of larger islets in their overall islet size distribution. Furthermore, islets exhibit a striking plasticity in cellular composition and architecture among different species and also within the same species under various pathophysiological conditions. In the present study, we describe novel approaches for the analysis of biological image data in order to facilitate the automation of analytic processes, which allow for the analysis of large and heterogeneous data collections in the study of such dynamic biological processes and complex structures. Such studies have been hampered due to technical difficulties of unbiased sampling and generating large-scale data sets to precisely capture the complexity of biological processes of islet biology. Here we show methods to collect unbiased "representative" data within the limited availability of samples (or to minimize the sample collection) and the standard experimental settings, and to precisely analyze the complex three-dimensional structure of the islet. Computer-assisted automation allows for the collection and analysis of large-scale data sets and also assures unbiased interpretation of the data. Furthermore, the precise quantification of islet size distribution and spatial coordinates (i.e. X, Y, Z-positions) not only leads to an accurate visualization of pancreatic islet structure and composition, but also allows us to identify patterns during development and adaptation to altering conditions through mathematical modeling. The methods developed in this study are applicable to studies of many other systems and organisms as well.

Novel computer-assisted methods of large-scale procurement and analysis of immunohistochemically stained pancreatic specimens are described: (1) Virtual Slice capture of the entire section; (2) Mass analysis of large-scale data; (3) Reconstruction of 2D Virtual Slices; (4) 3D islet mapping; and (5) Mathematical analysis.

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), ...

Single Particle Electron Microscopy Reconstruction of the Exosome Complex Using the Random Conical Tilt Method

Single particle electron microscopy (EM) reconstruction has recently become a popular tool to get the three-dimensional (3D) structure of large macromolecular complexes. Compared to X-ray crystallography, it has some unique advantages. First, single particle EM reconstruction does not need to crystallize the protein sample, which is the bottleneck in X-ray crystallography, especially for large macromolecular complexes. Secondly, it does not need large amounts of protein samples. Compared with milligrams of proteins necessary for crystallization, single particle EM reconstruction only needs several micro-liters of protein solution at nano-molar concentrations, using the negative staining EM method. However, despite a few macromolecular assemblies with high symmetry, single particle EM is limited at relatively low resolution (lower than 1 nm resolution) for many specimens especially those without symmetry. This technique is also limited by the size of the molecules under study, i.e. 100 kDa for negatively stained specimens and 300 kDa for frozen-hydrated specimens in general.
For a new sample of unknown structure, we generally use a heavy metal solution to embed the molecules by negative staining. The specimen is then examined in a transmission electron microscope to take two-dimensional (2D) micrographs of the molecules. Ideally, the protein molecules have a homogeneous 3D structure but exhibit different orientations in the micrographs. These micrographs are digitized and processed in computers as "single particles". Using two-dimensional alignment and classification techniques, homogenous molecules in the same views are clustered into classes. Their averages enhance the signal of the molecule's 2D shapes. After we assign the particles with the proper relative orientation (Euler angles), we will be able to reconstruct the 2D particle images into a 3D virtual volume.
In single particle 3D reconstruction, an essential step is to correctly assign the proper orientation of each single particle. There are several methods to assign the view for each particle, including the angular reconstitution1 and random conical tilt (RCT) method2. In this protocol, we describe our practice in getting the 3D reconstruction of yeast exosome complex using negative staining EM and RCT. It should be noted that our protocol of electron microscopy and image processing follows the basic principle of RCT but is not the only way to perform the method. We first describe how to embed the protein sample into a layer of Uranyl-Formate with a thickness comparable to the protein size, using a holey carbon grid covered with a layer of continuous thin carbon film. Then the specimen is inserted into a transmission electron microscope to collect untilted (0-degree) and tilted (55-degree) pairs of micrographs that will be used later for processing and obtaining an initial 3D model of the yeast exosome. To this end, we perform RCT and then refine the initial 3D model by using the projection matching refinement method3.

This article describes a standard method to get a three-dimensional (3D) reconstruction of biological macromolecules using negative staining electron microscopy (EM). In this protocol, we explain how to get the 3D structure of the Saccharomyces cerevisiae exosome complex at medium resolution using the random conical tilt reconstruction method (RCT).

Single particle electron microscopy (EM) reconstruction has recently become a popular tool to get the three-dimensional (3D) structure of large ...

Tangential Flow Ultrafiltration: A  &ldquo;Green&rdquo; Method for the Size Selection and Concentration of Colloidal Silver Nanoparticles

Nowadays, AgNPs are extensively used in the manufacture of consumer products,1 water disinfectants,2 therapeutics,1, 3 and biomedical devices4 due to their powerful antimicrobial properties.3-6 These nanoparticle applications are strongly influenced by the AgNP size and aggregation state. Many challenges exist in the controlled fabrication7 and size-based isolation4,8 of unfunctionalized, homogenous AgNPs that are free from chemically aggressive capping/stabilizing agents or organic solvents.7-13 Limitations emerge from the toxicity of reagents, high costs or reduced efficiency of the AgNP synthesis or isolation methods (e.g., centrifugation, size-dependent solubility, size-exclusion chromatography, etc.).10,14-18 To overcome this, we recently showed that TFU permits greater control over the size, concentration and aggregation state of Creighton AgNPs (300 ml of 15.3 &mu;g ml-1 down to 10 ml of 198.7 &mu;g ml-1) than conventional methods of isolation such as ultracentrifugation.19
TFU is a recirculation method commonly used for the weight-based isolation of proteins, viruses and cells.20,21 Briefly, the liquid sample is passed through a series of hollow fiber membranes with pore size ranging from 1,000 kD to 10 kD. Smaller suspended or dissolved constituents in the sample will pass through the porous barrier together with the solvent (filtrate), while the larger constituents are retained (retentate). TFU may be considered a "green" method as it neither damages the sample nor requires additional solvent to eliminate toxic excess reagents and byproducts. Furthermore, TFU may be applied to a large variety of nanoparticles as both hydrophobic and hydrophilic filters are available.
The two main objectives of this study were: 1) to illustrate the experimental aspects of the TFU approach through an invited video experience and 2) to demonstrate the feasibility of the TFU method for larger volumes of colloidal nanoparticles and smaller volumes of retentate. First, unfuctionalized AgNPs (4 L, 15.2 &mu;g ml-1) were synthesized using the well-established Creighton method22,23 by the reduction of AgNO3 with NaBH4. AgNP polydispersity was then minimized via a 3-step TFU using a 50-nm filter (460 cm2) to remove AgNPs and AgNP-aggregates larger than 50 nm, followed by two 100-kD (200 cm2 and 20 cm2) filters to concentrate the AgNPs. Representative samples were characterized using transmission electron microscopy, UV-Vis absorption spectrophotometry, Raman spectroscopy, and inductively coupled plasma optical emission spectroscopy. The final retentate consisted of highly concentrated (4 ml, 8,539.9 &mu;g ml-1) yet lowly aggregated and homogeneous AgNPs of 1-20 nm in diameter. This corresponds to a silver concentration yield of about 62%.

Tangential flow ultrafiltration (TFU) is a recirculation method used for the weight-based separation of biosamples. TFU was adapted to size-select (1-20 nm diameter) and highly concentrate a large volume of polydisperse silver nanoparticles (4 L of 15.2 &mu;g ml-1 down to 4 ml of 8,539.9 &mu;g ml-1) with minimal aggregation.

Nowadays, AgNPs are extensively used in the manufacture of consumer products,1 water disinfectants,2 therapeutics,1, 3 and biomedical devices4 due to ...

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools5,6. Canopy shade is an important environmental cue that affects plant architecture and life history; the suite of responses is collectively called the shade avoidance syndrome (SAS)7. Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices8. To date, leaf shape programs (e.g. SHAPE9, LAMINA10, LeafAnalyzer11, LEAFPROCESSOR12) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size13. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.

Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the ...

An Improved Method for Accurate and Rapid Measurement of Flight Performance in Drosophila

Drosophila has proven to be a useful model system for analysis of behavior, including flight. The initial flight tester involved dropping flies into an oil-coated graduated cylinder; landing height provided a measure of flight performance by assessing how far flies will fall before producing enough thrust to make contact with the wall of the cylinder. Here we describe an updated version of the flight tester with four major improvements. First, we added a &#34;drop tube&#34; to ensure that all flies enter the flight cylinder at a similar velocity between trials, eliminating variability between users. Second, we replaced the oil coating with removable plastic sheets coated in Tangle-Trap, an adhesive designed to capture live insects. Third, we use a longer cylinder to enable more accurate discrimination of flight ability. Fourth we use a digital camera and imaging software to automate the scoring of flight performance. These improvements allow for the rapid, quantitative assessment of flight behavior, useful for large datasets and large-scale genetic screens.

An Improved Method for Accurate and Rapid Measurement of Flight Performance in Drosophila

Here we describe a method for rapid and accurate measurement of flight performance in Drosophila, enabling high-throughput screening.

Drosophila has proven to be a useful model system for analysis of behavior, including flight. The initial flight tester involved dropping flies into an ...

An Immunofluorescent Method for Characterization of Barrett&#8217;s Esophagus Cells

Esophageal adenocarcinoma (EAC) has an overall survival rate of less than 17% and incidence of EAC has risen dramatically over the past two decades. One of the primary risk factors of EAC is Barrett&#8217;s esophagus (BE), a metaplastic change of the normal squamous esophagus in response to chronic heartburn. Despite the well-established connection between EAC and BE, interrogation of the molecular events, particularly altered signaling pathways involving progression of BE to EAC, are poorly understood. Much of this is due to the lack of suitable in vitro models available to study these diseases. Recently, immortalized BE cell lines have become commercially available allowing for in vitro studies of BE. Here, we present a method for immunofluorescent staining of immortalized BE cell lines, allowing in vitro characterization of cell signaling and structure after exposure to therapeutic compounds. Application of these techniques will help develop insight into the mechanisms involved in BE to EAC progression and provide potential avenues for treatment and prevention of EAC.

There is a discernible need for improved information on molecular drivers of Barrett&#8217;s Esophagus. Immunofluorescent staining is a useful technique for understanding the effects of cell signaling on cell morphology. We present a simple, effective protocol for the use of immunofluorescent staining to assess therapeutic treatment in Barrett&#8217;s Esophagus cells.

Esophageal adenocarcinoma (EAC) has an overall survival rate of less than 17% and incidence of EAC has risen dramatically over the past two decades. One ...

An Easy Method for Plant Polysome Profiling

Translation of mRNA to protein is a fundamental and highly regulated biological process. Polysome profiling is considered as a gold standard for the analysis of translational regulation. The method described here is an easy and economical way for fractionating polysomes from various plant tissues. A sucrose gradient is made without the need for a gradient maker by sequentially freezing each layer. Cytosolic extracts are then prepared in a buffer containing cycloheximide and chloramphenicol to immobilize the cytosolic and chloroplastic ribosomes to mRNA and are loaded onto the sucrose gradient. After centrifugation, six fractions are directly collected from the bottom to the top of the gradient, without piercing the ultracentrifugation tube. During collection, the absorbance at 260 nm is read continuously to generate a polysome profile that gives a snapshot of global translational activity. Fractions are then pooled to prepare three different mRNA populations: the polysomes, mRNAs bound to several ribosomes; the monosomes, mRNAs bound to one ribosome; and mRNAs that are not bound to ribosomes. mRNAs are then extracted. This protocol has been validated for different plants and tissues including Arabidopsis thaliana seedlings and adult plants, Nicotiana benthamiana, Solanum lycopersicum, and Oryza sativa leaves.

This protocol describes an easy method to extract and fractionate transcripts from plant tissues on the basis of the number of bound ribosomes. It allows a global estimate of translation activity and the determination of the translational status of specific mRNAs.

Translation of mRNA to protein is a fundamental and highly regulated biological process. Polysome profiling is considered as a gold standard for the ...

An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production

Monolayer cell culture does not adequately model the in vivo behavior of tissues, which involves complex cell-cell and cell-matrix interactions. Three-dimensional (3D) cell culture techniques are a recent innovation developed to address the shortcomings of adherent cell culture. While several techniques for generating tissue analogues in vitro have been developed, these methods are frequently complex, expensive to establish, require specialized equipment, and are generally limited by compatibility with only certain cell types. Here, we describe a rapid and flexible protocol for aggregating cells into multicellular 3D spheroids of consistent size that is compatible with growth of a variety of tumor and normal cell lines. We utilize varying concentrations of serum and methyl cellulose (MC) to promote anchorage-independent spheroid generation and prevent the formation of cell monolayers in a highly reproducible manner. Optimal conditions for individual cell lines can be achieved by adjusting MC or serum concentrations in the spheroid formation medium. The 3D spheroids generated can be collected for use in a wide range of applications, including cell signaling or gene expression studies, candidate drug screening, or in the study of cellular processes such as tumor cell invasion and migration. The protocol is also readily adapted to generate clonal spheroids from single cells, and can be adapted to assess anchorage-independent growth and anoikis-resistance. Overall, our protocol provides an easily modifiable method for generating and utilizing 3D cell spheroids in order to recapitulate the 3D microenvironment of tissues and model the in vivo growth of normal and tumor cells.

Here, we describe a rapid and flexible protocol for the formation of 3D cell spheroids through cell aggregation. This is easily adapted to multiple cell types and is suitable for use in a variety of applications including cell migration, invasion, or anoikis assays, and for imaging and quantifying cell-matrix interactions.

Monolayer cell culture does not adequately model the in vivo behavior of tissues, which involves complex cell-cell and cell-matrix interactions. ...

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, log-phase, and stationary-phase. Each growth phase can reveal different aspects of fitness that are related to various environmental and genetic conditions. High-resolution and quantitative measurements of all 3 phases of growth are generally difficult to obtain. Here we present a detailed method to characterize all 3 growth phases on solid media using an assay called One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). ODELAY quantifies growth phenotypes of individual cells growing into colonies on solid media using time-lapse microscopy. This method can directly observe population heterogeneity with each growth parameter in genetically identical cells growing into colonies. This population heterogeneity offers a unique perspective for understanding genetic and epigenetic regulation, and responses to genetic and environmental perturbations. While the ODELAY method is demonstrated using yeast, it can be utilized on any colony forming microorganism that is visible by bright field microscopy.

We present a method for quantifying growth phenotypes of individual yeast cells as they grow into colonies on solid media using time-lapse microscopy termed, One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). Population heterogeneity of genetically identical cells growing into colonies can be directly observed and quantified.

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, ...