The procedure to analyze and sort Streptomyces pellets from a two-day-old liquid-grown culture is schematically represented in Figure 1. Details for the method are given below.
1. Growth (Including Preparation of Media and Buffers)
<ol>
	<li>Prepare 1 L of 10x&#160;phosphate buffered saline (PBS; 80 g NaCl, 2 g KCl, 14.4 g Na2HPO4, 2.4 g KH2PO4 in 1 L of distilled water adjusted to pH 7.4). At time of use add 100 ml of 10x&#160;PBS to 900 ml of distilled water to obtain 1 L of PBS.</li>
	<li>Prepare 1 L of YEME medium (3 g Difco yeast extract, 5 g Bacto peptone, 3 g Oxoid malt extract, 10 g glucose, 340 g sucrose, distilled water up to 1 L) and 100 ml of 2.5 M MgCl2. Sterilize both solutions by autoclaving. Note that also other Streptomyces media can be used4,14.</li>
	<li>Add 100 ml YEME medium and 0.2 ml 2.5 M MgCl2 to a sterile 250 ml Erlenmeyer flask equipped with metal coiled springs. Prepare as many flasks as bacterial samples to study.</li>
	<li>Inoculate each flask with 108 spores of Streptomyces to obtain a spore concentration of 106 spores/ml. Grow the bacteria for 2 days at 30 &#176;C while shaking at 180 rpm.</li>
</ol>
2. Sampling
<ol>
	<li>Transfer a 5 ml sample from each culture to a 15 ml Falcon tube using a sterile 5 ml pipette. Gently shake the culture while taking the sample to ensure that the sample is representative for the whole culture.</li>
	<li>When samples are analyzed immediately with COPAS no sample preparation steps are necessary. Keep the sample on ice and proceed to step 3.1 of this protocol. When samples are analyzed at a later time point, fix the pellets as described in steps 2.3-2.5 of this protocol. Note that fixation also prevents growth of the bacteria in the tubing system when not properly cleaned after analyses. Ensure that fixation with formaldehyde is not hampering downstream analyses. For instance, RNA could not be isolated from fungal pellets after fixation with formaldehyde. In contrast, RNA was successfully isolated after fixation with 70% ethanol12.</li>
	<li>Add 600 &#956;l of 37% formaldehyde to 5 ml of sample and mix by inverting the tube 3x. Fix the samples on ice for 30 min.</li>
	<li>Centrifuge the samples at 2,500 x g in a fixed rotor at 4 &#176;C for 10 min. Carefully remove the supernatant and wash the pellets with 5 ml of PBS.</li>
	<li>Repeat step 2.4 so that the pellets are washed with PBS 2x. The samples can be stored in PBS at 4 &#176;C for several weeks.</li>
</ol>
3. COPAS Analysis
<ol>
	<li>Make sure the COPAS Plus, the pump, the computer and the 488 nm argon laser are turned on and start the Biosort software. Although turned on, the laser will still be in standby modus.</li>
	<li>Check if the sheath fluid bottle is full and the waste bottle is empty.</li>
	<li>Click &#39;START&#39; and then &#39;RUN&#39; to switch the 488 nm argon laser from standby to on. After approximately 60 sec, when the laser is on, click &#39;DONE&#39;.</li>
	<li>Check the pressure gauges. The pressures for &#39;Sheath&#39;, &#39;Sample&#39;, &#39;Sorter&#39; and &#39;Clean&#39; should read around 4.0, 0.5, 2.7, and 10.9 respectively.</li>
	<li>Click the checkbox next to &#39;Pressure OK&#39;. The system then primes the flow cell and is ready for use.</li>
	<li>Standard settings are assumed with &#39;Delay&#39; at 11 and &#39;Width&#39; 7. Thresholds are set at 50 for &#39;Signal&#39; and 40 for &#39;TOF Minimum&#39;, with TOF representing the time-of-flight. (For completeness: all &#39;Gains&#39; are set at 1, the trigger is set at EXT (Extinction) and in the &#39;Configuration&#39; tab under &#39;Select scan rate&#39; 2.50 MHz is chosen. Coincidence is set to &#39;Enhanced&#39;).</li>
	<li>Remove leftover water from the sample cup and add 0.1 ml of Streptomyces sample from step 2.2 or 2.5 in the cup together with PBS (around 50 ml). Make sure the sample cup is closed properly.</li>
	<li>Click &#39;Acquire&#39; to start collecting data. Manage the flow speed to obtain between 30-50 events per second. If the flow is too high, add PBS to the sample cup. If the flow speed is too low, add more sample.</li>
	<li>When at least 2,500 events are collected click &#39;STOP&#39;. To save all data click &#39;Store&#39; and save your file for subsequent statistical analysis. The Biosort software will save the data in four files, of which only the .txt file will be used for the subsequent analysis.</li>
	<li>Clean the sample cup by removing the sample with a 50 ml syringe and washing with water 2x.</li>
	<li>Repeat steps 3.7-3.10 until all samples are analyzed.</li>
</ol>
4. Data Analysis
<ol>
	<li>Open the .txt file and discard data with an EXT lower than 25 (using for instance MS Excel). This corresponds to debris and loose hyphal fragments (for Aspergillus niger all data with an EXT lower than 150 are discarded)4,12. Then, save all TOFs in one column in a plain &#39;.txt&#39; file (for many data files this can be automated in the programming language R, available from: <a href="http://www.r-project.org">http://www.r-project.org</a>).</li>
	<li>Make sure SciLab (version 5.3.2 or newer from http://www.scilab.org/) is installed together with the &#39;fittools&#39; library, which is available, together with the manual, from: 
	http://web.science.uu.nl/microbiology/images/fung/fittools.zip 
	http://web.science.uu.nl/microbiology/images/fung/manual%20fittools.pdf</li>
	<li>Start SciLab and run the necessary functions of the fittools library to model the data. In short: Install the fittools library as shown in the manual (only needed once).
	<ol>
		<li>Load the fittools library.</li>
		<li>Read the data (&#39;.txt&#39; file) into SciLab.</li>
		<li>Log transform the data (natural log is the default log function).</li>
		<li>Fit the model of 2 populations to the data.</li>
		<li>Plot a graph with the histogram and model (optional).</li>
		<li>Bootstrap the data (1,000 replicates).</li>
		<li>Fit the model to the bootstrapped datasets.</li>
		<li>Get the confidence intervals estimates of the 5 parameters. This will provide information on the participation fraction (p) of the populations, their 2 means (&#956;1 and &#956;2) and the accompanying standard deviations (&#945;1 and &#945;2). Furthermore, the 95% confidence intervals are determined by refitting with the model after bootstrapping (1,000 replicates)12,13,15. When the confidence intervals of the means do not overlap and the confidence interval of p is between 0.025-0.975 the dataset can be explained as a combination of two populations of normal distributions. (Note that the participation fraction of small pellets is p, whereas the participation of large pellets is 1-p.) The relationship between TOF and the diameter of a Streptomyces pellet equals 0.57 &#215; TOF + 159 &#956;m4.</li>
	</ol>
	</li>
	<li>In the case two populations are found use the average TOFs as sorting parameters in the COPAS as the upper and lower bound to sort the small and large pellets subsequently. Note that also other sorting parameters can be selected depending on the demands of the user.</li>
</ol>
5. Pellet Sorting
<ol>
	<li>Load the Streptomyces sample that needs to be sorted as described in step 3.7.</li>
	<li>Set &#39;Sorting Limits&#39; and provide details about the minimal (Lo) and maximal (Hi) TOF values. Alternatively, set an area by selecting &#39;Regions&#39; and then &#39;Define Gate Region&#39; to select the pellets with the desired dimensions or properties.</li>
	<li>Place a 50 ml tube to collect multiple pellets that meet the sorting parameters. Alternatively, a micro-titer plate can be used to sort individual pellets. Multiple pellets (104-105) are needed for DNA and protein extraction. A single pellet is sufficient for qPCR analysis of gene expression, but multiple pellets are needed for RNA sequencing.</li>
	<li>Decide and set the number of pellets that need to be sorted and click &#39;Sort Manually&#39; to sort for the selected parameters. When the set amount of pellets is reached, the sorting ends automatically.</li>
	<li>Remove left over sample with a 50 ml syringe from the sample cup and wash the cup with water 2x. Repeat steps 5.1-5.5 if other samples need to be sorted.</li>
	<li>Before shutting down the COPAS rinse the sample cup with water to remove the remaining pellets and clean the sample cup with 70% EtOH and/or 2% hypochlorite in water. Run a sample of chlorinated water to clean the entire system and repeat this with water. Empty the overflow container and clean or replace the filter that resides in this container.</li>
	<li>When finished, click &#39;STOP&#39; to release the pressure from the system. When the motor stops close the program and click &#39;Shutdown without purging&#39;. Then switch off the computer, the COPAS, the laser, and the pump.</li>
</ol>

<ol>
	<li>Hopwood, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Streptomyces in nature and medicine: the antibiotic makers. , (2007).</li><li>van Wezel, G. P., McDowall, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+regulation+of+the+secondary+metabolism+of+Streptomyces:+new+links+and+experimental+advances.">The regulation of the secondary metabolism of Streptomyces: new links and experimental advances.</a> Nat. Prod. Rep. 28, 1311-1333 (2011).</li><li>Ann&#233;, J., Maldonado, B., Van Impe, J., Van Mellaert, L., Bernaerts, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recombinant+protein+production+and+streptomycetes.">Recombinant protein production and streptomycetes.</a> J. Biotechnol. 158, 159-167 (2012).</li><li>van Veluw, G. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+two+distinct+mycelial+populations+in+liquid-grown+Streptomyces+cultures+using+a+flow+cytometry-based+proteomics+approach.">Analysis of two distinct mycelial populations in liquid-grown Streptomyces cultures using a flow cytometry-based proteomics approach.</a> Appl. Microbiol. Biotechnol. 96, 1301-1312 (2012).</li><li>Veening, J. W., Smits, W. K., Bistability Kuipers, O. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=epigenetics,+and+bet-hedging+in+bacteria.">epigenetics, and bet-hedging in bacteria.</a> Annu. Rev. Microbiol. 62, 193-210 (2008).</li><li>Davey, H. M., Winson, M. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+flow+cytometry+to+quantify+microbial+heterogeneity.">Using flow cytometry to quantify microbial heterogeneity.</a> Curr. Issues Mol. Biol. 5, 9-15 (2003).</li><li>Betz, J. W., Aretz, W., H&#228;rtel, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+flow+cytometry+in+industrial+microbiology+for+strain+improvement+programs.">Use of flow cytometry in industrial microbiology for strain improvement programs.</a> Cytometry. 5, 145-150 (1984).</li><li>Bell, P. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+flow+cytometric+method+for+rapid+selection+of+novel+industrial+yeast+hybrids.">A flow cytometric method for rapid selection of novel industrial yeast hybrids.</a> Appl. Environ. Microbiol. 64, 1669-1672 (1998).</li><li>Pulak, R., Strange, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Techniques+for+analysis,+sorting,+and+dispensing+of+C.+elegans+on+the+COPAS+flow-sorting+system.">Techniques for analysis, sorting, and dispensing of C. elegans on the COPAS flow-sorting system.</a> C. elegans: methods and applications. , 275-286 (2006).</li><li>Buszczak, M., 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=The+carnegie+protein+trap+library:+a+versatile+tool+for+Drosophila+developmental+studies.">The carnegie protein trap library: a versatile tool for Drosophila developmental studies.</a> Genetics. 175, 1505-1531 (2007).</li><li>Carvalho, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+high-throughput+screen+for+tuberculosis+progression.">A high-throughput screen for tuberculosis progression.</a> PLoS One. 6, (2011).</li><li>de Bekker, C., van Veluw, G. J., Vinck, A., Wiebenga, L. A., W&#246;sten, H. A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heterogeneity+of+Aspergillus+niger+microcolonies+in+liquid+shaken+cultures.">Heterogeneity of Aspergillus niger microcolonies in liquid shaken cultures.</a> Appl. Environ. Microbiol. 77, 1263-1267 (2011).</li><li>van Veluw, G. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heterogeneity+in+liquid+shaken+cultures+of+Aspergillus+niger+inoculated+with+melanised+conidia+or+conidia+of+pigmented+mutants.">Heterogeneity in liquid shaken cultures of Aspergillus niger inoculated with melanised conidia or conidia of pigmented mutants.</a> Stud. Mycol. 74, 47-57 (2013).</li><li>Kieser, T., Bibb, M. J., Buttner, M. J., Chater, K. F., Hopwood, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Practical Streptomyces Genetics. , (2000).</li><li>Vinck, 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=Hyphal+differentiation+in+the+exploring+mycelium+of+Aspergillus+niger.">Hyphal differentiation in the exploring mycelium of Aspergillus niger.</a> Mol. Microbiol. 58, 693-699 (2005).</li><li>Reichl, U., King, R., Gilles, E. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+pellet+morphology+during+submerged+growth+of+Streptomyces+tendae+by+image+analysis.">Characterization of pellet morphology during submerged growth of Streptomyces tendae by image analysis.</a> Biotechnol. Bioeng. 39, 164-170 (1992).</li></ol>

The authors declare that they have no competing financial interests.

Flow cytometry has enabled the high-speed analysis of large numbers of single cells, which contributed to our understanding of heterogeneity in clonal populations6. Regular flow cytometry is not feasible for analysis of the multicellular mycelial pellets of streptomycetes and fungi. Our work has demonstrated that high-throughput analysis of Streptomyces pellets is feasible using COPAS. The procedure outlined here is simple, fast, and highly reproducible. The critical parameter to keep in mind during operation of the instrument is the flow speed, which should not exceed 100 events/sec&#160;(step 3.8 of this protocol). If the pellet concentration, and therefore also the flow speed, becomes too high, the TOF values will be miscalculated because the instrument fails to detect individual pellets. Sufficiently diluting the sample by the addition of PBS overcomes this problem.
Limitations 
The COPAS Plus used here has a nozzle diameter of 1 mm, which is suitable for measuring particles with a size ranging from 30-700 &#956;m. This nozzle therefore enables measuring pellets formed by streptomycetes. In the case of filamentous fungi the micro-colonies may be larger, which limits the general applicability of the COPAS Plus. The COPAS XL can measure particles up to 1,500 &#956;m in size but its sensitivity in the lower range of diameter is less compared to that of the COPAS Plus. Both the COPAS Plus and the COPAS XL cannot analyze particles smaller than 30 &#956;m. This implies that individual microbial spores or cells cannot be analyzed. In addition, the COPAS may not accurately analyze small aggregates of spores and cells or the very small micro-colonies. For this, regular cell analyzers should be used. This limitation is overcome by the Biosorter of Union Biometrica, which can analyze particles in the range of 1-1,500 &#956;m. The purchase prize however is higher.
Troubleshooting 
The COPAS is a robust instrument that is easy to operate. However, sometimes pellets are not detected after loading a sample into the sample cup and starting the measurement. The cause is typically a clogged entry tube, which can easily be solved by pressing the &#39;clean&#39; command. This will force the pellets back from the tubing system into the sample cup. An alternative reason might be that the lid is not properly placed on the sample cup. This leads to pressure loss and the concomitant failure to detect pellets.
Significance and future directions 
We here focused on the analysis of pellet size but the COPAS setup is also able to analyze and sort based on fluorescence and density. Fluorescence detection enables us to analyze gene expression based on reporters such as GFP. Furthermore, the composition of cells can be evaluated. Even more powerful is the option to separate pellets according to these parameters. Sorted pellets can be used for downstream analyses, including all -omics studies. Indeed, we previously sorted 60,000 large and 200,000 small pellets, and demonstrated that the proteome was significantly different between large and small pellets4. This technology therefore provides new leads to improve streptomycetes as cell factories.
The key benefit of the COPAS technology is time. Previous studies on cell pellets were performed using microscopy, and this limits the number of pellets that can be analyzed up to several hundreds16. Microscopy studies already suggested the existence of two populations of pellets in liquid-grown Streptomyces cultures16. Indeed, two populations of pellets were detected regardless of culture conditions in a large number of different streptomycetes4. This size heterogeneity is not restricted to filamentous streptomycetes, but was also observed in filamentous fungi12. In all cases, the underlying mechanisms of heterogeneity are not yet known. The possibility to sort pellets according to size and fluorescence enables us to unravel such mechanisms.

Streptomycetes are filamentous soil bacteria that are well known for their proficiency to make antibiotics, as well as compounds that can be used as immunosuppressants or to combat fungal infections or cancer1,2. In addition, these organisms produce enzymes that are of interest for a wide range of industrial applications3. Most of these commercially interesting compounds are produced in bioreactors. Growth of streptomycetes in bioreactors is characterized by the formation of complex structures of interconnected hyphae, known as clumps or pellets. These multicellular structures are highly heterogeneous with respect to size4 and can reach sizes that are more than a million times larger than a single-celled bacterium such as Escherichia coli or Bacillus subtilis. Although heterogeneity is regarded as a beneficial trait in natural biological systems5, it is considered a production pitfall in industry. Bioreactor cultivations have to be reproducible and controllable to obtain the highest possible yields. A detailed understanding of the role of each of the pellet types in a bioreactor is therefore crucial to improve streptomycetes as a cell factory.
Flow cytometry is commonly used to analyze individual cells in a population6. Flow cytometers can acquire multiparametric information by simultaneously measuring characteristics of the cells (such as size, density, and multicolor fluorescence). In this way, cell properties can be correlated thereby contributing to our understanding of heterogeneity within a culture and the existence of distinct populations of cells6. More specialized instruments have made it possible to sort cells according to user-defined parameters. For instance, mutants can be screened. Following sorting, such mutant cells can be cultivated for further characterization. This has already proven to be useful, amongst others, to improve productivity of strains7,8. The nozzles of flow cytometers typically allow for the passage of cells with a maximal diameter of approximately 10 &#956;m. Therefore, pellets of streptomycetes cannot be analyzed with regular flow cytometers. They, however, can be analyzed with the Complex Object Parametric Analyzer and Sorter (COPAS). Like regular flow cytometers, COPAS can acquire multiparametric data of particles in a high throughput manner. Depending on the type of COPAS 10-1,500 &#956;m sized particles can be analyzed. In addition, it allows for sorting of individual particles that can be used for cultivation or downstream analyses, such as the isolation of DNA, RNA, or proteins. COPAS was initially designed for the analysis and sorting of small multicellular organisms, such as the nematode Caenorhabditis elegans9, and Drosophila embryos and larvae10. The instruments have also been used for zebrafish11 and for filamentous fungi12,13. The latter organisms also form mycelial pellets that are even larger than those formed by filamentous bacteria. We have recently demonstrated that the use of COPAS is also feasible for streptomycetes4. We here describe the experimental procedure for using the COPAS to assess pellet heterogeneity in Streptomyces coelicolor, including details on the methodology to sort pellets according to size. Please note, however, that this method can also be used for the analysis of other pellet-forming streptomycetes.

<table border="0" cellpadding="0" cellspacing="0" width="992">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:212px;">Copas PLUS</td>
			<td style="width:183px;">Union Biometrica</td>
			<td style="width:119px;">PLUS</td>
			<td style="width:479px;">Large particle flow cytometer including lasers and software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">NaCl</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>S3014</td>
			<td>PBS component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">KCl</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>P9541</td>
			<td>PBS component</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">Na2HPO4</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>S3264</td>
			<td>PBS component</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">KH2PO4</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>P9791</td>
			<td>PBS component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Difco Yeast Extract</td>
			<td>BD Biosciences</td>
			<td>210933</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Bacto Peptone</td>
			<td>BD Biosciences</td>
			<td>211677</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Oxoid Malt Extract</td>
			<td>Fisher Scientific</td>
			<td>OXLP0039B</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Glucose</td>
			<td>Sigma Aldrich</td>
			<td>G8270</td>
			<td>Media component</td>
		</tr>
		<tr height="23">
			<td height="23" style="height:23px;">Sucrose</td>
			<td>Sigma Aldrich</td>
			<td>S9378</td>
			<td>Media component</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">MgCl2.6H2O</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>M2670</td>
			<td>Media components. Add after autoclaving.</td>
		</tr>
		<tr height="24">
			<td height="24" style="height:24px;width:212px;">Formaldehyde Solution</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>F8775</td>
			<td style="width:479px;">Fixation of pellets</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Sodium Hypochlorite Solution&#160;</td>
			<td>Sigma Aldrich</td>
			<td style="width:119px;">71696</td>
			<td>For cleaning of the instrument</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EtOH</td>
			<td style="width:183px;">VWR</td>
			<td style="width:119px;">20816.367</td>
			<td>For cleaning of the instrument</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:212px;">Erlenmeyer Flask (250 ml)</td>
			<td style="width:183px;">Fisher Scientific</td>
			<td>214-1132</td>
			<td>Culture flask for growing Streptomyces</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:212px;">Springs</td>
			<td style="width:183px;">Verenfabriek De Spiraal</td>
			<td style="width:119px;">Custom-Made</td>
			<td>Used in culture flask. RVS1.4401/Length 210 mm/Diameter 17 mm/Pitch 5 mm</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:212px;">Sterile Centrifuge Tube (15 ml)</td>
			<td style="width:183px;">Sarstedt</td>
			<td>62.554.002</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:212px;">Syringes (50 ml)</td>
			<td style="width:183px;">Sigma</td>
			<td>Z683698&#160;</td>
			<td></td>
		</tr>
	</tbody>
</table>

sorting of streptomyces cell pellets using a complex object parametric analyzer and sorter

Streptomycetes are filamentous soil bacteria that are used in industry for the production of enzymes and antibiotics. When grown in bioreactors, these organisms form networks of interconnected hyphae, known as pellets, which are heterogeneous in size. Here we describe a method to analyze and sort mycelial pellets using a Complex Object Parametric Analyzer and Sorter (COPAS). Detailed instructions are given for the use of the instrument and the basic statistical analysis of the data. We furthermore describe how pellets can be sorted according to user-defined settings, which enables downstream processing such as the analysis of the RNA or protein content. Using this methodology the mechanism underlying heterogeneous growth can be tackled. This will be instrumental for improving streptomycetes as a cell factory, considering the fact that productivity correlates with pellet size.

COPAS measurements of Streptomyces pellets 
Streptomycetes form mycelial pellets in liquid cultures that have a wide range of sizes. To analyze pellet size distribution, a 2-day old liquid grown Streptomyces coelicolor culture was subjected to large particle flow cytometry using a COPAS Plus Profiler equipped with a 1 mm nozzle. A typical COPAS output is a scatter plot such as visualized in Figure&#160;2A. The x-axis represents the time-of-flight (TOF), which correlates with pellet size (i.e. it takes longer for larger pellets to pass the laser beam). The y-axis shows the extinction, which represents the optical density of the object. Each point in the scatter plot corresponds to an individual event, i.e. a single pellet passing the laser beam. Importantly, when the sample is too concentrated (i.e. when the laser detects more than 100 events/sec), the COPAS frequently fails to detect individual pellets. This leads to false TOF values that are too high. Diluting the sample reduces the average TOF values, up to a point where further diluting does no longer impact TOF. This point is reached when approximately 100 pellets/sec&#160;were analyzed.
Plotting the data points into a histogram indicates that the sizes are not normally distributed (Figure&#160;2B). The distribution appears to be skewed to the right. Log transforming the data set also did not lead to a normal distribution (Figure&#160;2C). To assess whether the size distribution can be explained by assuming a mixture of two normal distributions the data was mathematically modeled12,15. Modeling indeed indicated that the size distribution can be explained by assuming the existence of two distinct populations of pellets. The population of small pellets consisted of 92% of all pellets with an average size of 248 &#956;m, while the population of large pellets (8% of all pellets) had an average size of 319 &#956;m (note that two populations are assumed to exist when the participation fraction is between 2.5-97.5%). 
Sorting of Streptomyces pellets according to size
Micro-colonies from the populations of large and small pellets were sorted. To this end, the average pellet sizes of the two populations were used to define the boundaries for sorting. Pellets smaller than 248 &#956;m were considered to be from the small pellet population, whereas pellets larger than 319 &#956;m were considered to be from the large pellet population (Figure&#160;3), so as to limit sorting of pellets from the overlapping portion of the two size distributions. Microscopic analysis of sorted pellets showed their distinct sizes (Figure&#160;3). The collected pellets can be further used for DNA, RNA, or protein isolations.
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/51178/51178fig1highres.jpg" src="/files/ftp_upload/51178/51178fig1.jpg" /> 
Figure 1. Schematic representation of the experimental setup to measure and analyze Streptomyces pellets using COPAS. For details, see experimental procedures.&#160;<a href="https://www.jove.com/files/ftp_upload/51178/51178fig1highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 2" fo:content-width="5in" fo:src="/files/ftp_upload/51178/51178fig2highres.jpg" src="/files/ftp_upload/51178/51178fig2.jpg" /> 
Figure 2. Pellet size heterogeneity in liquid-grown Streptomyces cultures. COPAS analysis of a 2-day-old S. coelicolor YEME culture (A) indicates that pellet sizes (indicated as Time of Flight values) are not normally distributed (B), and neither become so when log-transformed (C). Instead, two populations of pellets that differ in TOF (and thus size) were detected. <a href="https://www.jove.com/files/ftp_upload/51178/51178fig2highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 3" fo:content-width="5in" fo:src="/files/ftp_upload/51178/51178fig3highres.jpg" src="/files/ftp_upload/51178/51178fig3.jpg" /> 
Figure 3. Fractionation of Streptomyces pellets according to size. Pellets with a size smaller than 248 &#956;m (indicated in pink) were considered to be small pellets, whereas pellets with a size larger than 319 &#956;m (indicated in blue) were considered to be large pellets. Microscopic analysis confirmed the difference in size. Note that these sizes were calculated from the log-transformed data using the described relationship between TOF and the diameter of a Streptomyces pellet, which equals 0.57 &#215; TOF + 159 &#956;m. <a href="https://www.jove.com/files/ftp_upload/51178/51178fig3highres.jpg" target="_blank">Click here to view larger image.</a>

Watch this Scientific Journal Video about Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter at JoVE.com

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

Liquid-grown Streptomyces cultures are characterized by mycelial pellets that are heterogeneous in size. We here describe a method to analyze and sort such pellets in a high-throughput manner. These pellets can be used for further analyses, which will provide leads to understand and control growth heterogeneity.

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

Streptomycetes are filamentous soil bacteria that are used in industry for the production of enzymes and antibiotics. When grown in bioreactors, these ...

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Nanyang Technological University

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Biology

10.9K Views.  Leiden University. Liquid-grown Streptomyces cultures are characterized by mycelial pellets that are heterogeneous in size. We here describe a method to analyze and sort such pellets in a high-throughput manner. These pellets can be used for further analyses, which will provide leads to understand and control growth heterogeneity.

Video: Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

A Multi-Parametric Islet Perifusion System within a Microfluidic Perifusion Device

A microfluidic islet perifusion device was developed for the assessment of dynamic insulin secretion of multiple islets and simultaneous fluorescence imaging of calcium influx and mitochondrial potential changes. The device consists of three layers: first layer contains an array of microscale wells (500 &mu;m diameter and 150 &mu;m depth) that help to immobilize the islets while exposed to flow and maximize the exposed surface area of the islets; the second layer contains a circular perifusion chamber (3 mm deep, 7 mm diameter); and the third layer contains an inlet-mixing channel that fans out before injection into the perifusion chamber (2 mm in width, 19 mm in length, and 500 &mu;m in height) for optimizing the mixing efficiency prior to entering the perifusion chamber. The creation of various glucose gradients including a linear, bell shape, and square shapes also can be created in the microfluidic perifusion network and is demonstrated.

A microfluidic islet perifusion device was developed for the assessment of dynamic insulin secretion of multiple islets and simultaneous fluorescence imaging of calcium influx and mitochondrial potential changes.

A microfluidic islet perifusion device was developed for the assessment of dynamic insulin secretion of multiple islets and simultaneous fluorescence ...

Fluorescence Activated Cell Sorting of Plant Protoplasts

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental stimuli in any multicellular organism. In situ hybridization and reporter gene visualization can to a limited extent be used to this end but for high resolution quantitative RT-PCR or high-throughput transcriptome-wide analysis the isolation of RNA from particular cell types is requisite. Cellular dissociation of tissue expressing a fluorescent protein marker in a specific cell type and subsequent Fluorescence Activated Cell Sorting (FACS) makes it possible to collect sufficient amounts of material for RNA extraction, cDNA synthesis/amplification and microarray analysis.
An extensive set of cell type-specific fluorescent reporter lines is available to the plant research community. In this case, two marker lines of the Arabidopsis thaliana root are used: PSCR::GFP (endodermis and quiescent center) and PWOX5::GFP (quiescent center). Large numbers (thousands) of seedlings are grown hydroponically or on agar plates and harvested to obtain enough root material for further analysis. Cellular dissociation of plant material is achieved by enzymatic digestion of the cell wall. This procedure makes use of high osmolarity-induced plasmolysis and commercially available cellulases, pectinases and hemicellulases to release protoplasts into solution.
FACS of GFP-positive cells makes use of the visualization of the green versus the red emission spectra of protoplasts excited by a 488 nm laser. GFP-positive protoplasts can be distinguished by their increased ratio of green to red emission. Protoplasts are typically sorted directly into RNA extraction buffer and stored for further processing at a later time.
This technique is revealed to be straightforward and practicable. Furthermore, it is shown that it can be used without difficulty to isolate sufficient numbers of cells for transcriptome analysis, even for very scarce cell types (e.g. quiescent center cells). Lastly, a growth setup for Arabidopsis seedlings is demonstrated that enables uncomplicated treatment of the plants prior to cell sorting (e.g. for the cell type-specific analysis of biotic or abiotic stress responses). Potential supplementary uses for FACS of plant protoplasts are discussed.

A method for isolating specific cell types from plant material is demonstrated. This technique employs transgenic marker lines expressing fluorescent proteins in particular cell types, cellular dissociation and Fluorescence Activated Cell Sorting. Additionally, a growth setup is established here that facilitates treatment of Arabidopsis thaliana seedlings prior to cell sorting.

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental ...

Lipid Vesicle-mediated Affinity Chromatography using Magnetic Activated Cell Sorting (LIMACS): a Novel Method to Analyze Protein-lipid Interaction

The analysis of lipid protein interaction is difficult because lipids are embedded in cell membranes and therefore, inaccessible to most purification procedures. As an alternative, lipids can be coated on flat surfaces as used for lipid ELISA and Plasmon resonance spectroscopy. However, surface coating lipids do not form microdomain structures, which may be important for the lipid binding properties. Further, these methods do not allow for the purification of larger amounts of proteins binding to their target lipids.
 To overcome these limitations of testing lipid protein interaction and to purify lipid binding proteins we developed a novel method termed lipid vesicle-mediated affinity chromatography using magnetic-activated cell sorting (LIMACS). In this method, lipid vesicles are prepared with the target lipid and phosphatidylserine as the anchor lipid for Annexin V MACS. Phosphatidylserine is a ubiquitous cell membrane phospholipid that shows high affinity to the protein Annexin V. Using magnetic beads conjugated to Annexin V the phosphatidylserine-containing lipid vesicles will bind to the magnetic beads. When the lipid vesicles are incubated with a cell lysate the protein binding to the target lipid will also be bound to the beads and can be co-purified using MACS. This method can also be used to test if recombinant proteins reconstitute a protein complex binding to the target lipid. 
 We have used this method to show the interaction of atypical PKC (aPKC) with the sphingolipid ceramide and to co-purify prostate apoptosis response 4 (PAR-4), a protein binding to ceramide-associated aPKC. We have also used this method for the reconstitution of a ceramide-associated complex of recombinant aPKC with the cell polarity-related proteins Par6 and Cdc42. Since lipid vesicles can be prepared with a variety of sphingo- or phospholipids, LIMACS offers a versatile test for lipid-protein interaction in a lipid environment that resembles closely that of the cell membrane. Additional lipid protein complexes can be identified using proteomics analysis of lipid binding protein co-purified with the lipid vesicles.

Lipid Vesicle-mediated Affinity Chromatography using Magnetic Activated Cell Sorting LIMACS: a Novel Method to Analyze Protein-lipid Interaction

To test the interaction of a protein with its target lipid we used MACS and Annexin V-conjugated magnetic beads and lipid vesicles synthesized from the target lipid and Annexin V-binding phosphatidylserine. Proteins bound to the target lipid are co-purified and analyzed after elution from the beads.

The analysis of lipid protein interaction is difficult because lipids are embedded in cell membranes and therefore, inaccessible to most purification ...

Cell Specific Analysis of Arabidopsis Leaves Using Fluorescence Activated Cell Sorting

After initiation of the leaf primordium, biomass accumulation is controlled mainly by cell proliferation and expansion in the leaves1. However, the Arabidopsis leaf is a complex organ made up of many different cell types and several structures. At the same time, the growing leaf contains cells at different stages of development, with the cells furthest from the petiole being the first to stop expanding and undergo senescence1. Different cells within the leaf are therefore dividing, elongating or differentiating; active, stressed or dead; and/or responding to stimuli in sub-sets of their cellular type at any one time. This makes genomic study of the leaf challenging: for example when analyzing expression data from whole leaves, signals from genetic networks operating in distinct cellular response zones or cell types will be confounded, resulting in an inaccurate profile being generated.
To address this, several methods have been described which enable studies of cell specific gene expression. These include laser-capture microdissection (LCM)2 or GFP expressing plants used for protoplast generation and subsequent fluorescence activated cell sorting (FACS)3,4, the recently described INTACT system for nuclear precipitation5 and immunoprecipitation of polysomes6.
FACS has been successfully used for a number of studies, including showing that the cell identity and distance from the root tip had a significant effect on the expression profiles of a large number of genes3,7. FACS of GFP lines have also been used to demonstrate cell-specific transcriptional regulation during root nitrogen responses and lateral root development8, salt stress9 auxin distribution in the root10 and to create a gene expression map of the Arabidopsis shoot apical meristem11. Although FACS has previously been used to sort Arabidopsis leaf derived protoplasts based on autofluorescence12,13, so far the use of FACS on Arabidopsis lines expressing GFP in the leaves has been very limited4. In the following protocol we describe a method for obtaining Arabidopsis leaf protoplasts that are compatible with FACS while minimizing the impact of the protoplast generation regime. We demonstrate the method using the KC464 Arabidopsis line, which express GFP in the adaxial epidermis14, the KC274 line, which express GFP in the vascular tissue14 and the TP382 Arabidopsis line, which express a double GFP construct linked to a nuclear localization signal in the guard cells (data not shown; Figure 2). We are currently using this method to study both cell-type specific expression during development and stress, as well as heterogeneous cell populations at various stages of senescence.

Cell Specific Analysis of Arabidopsis Leaves Using Fluorescence Activated Cell Sorting

A method for producing Arabidopsis leaf protoplasts that are compatible with fluorescence activated cell sorting (FACS), allowing for studies of specific cell populations. This method is compatible with any Arabidopsis line that expresses GFP in a subset of cells.

After initiation of the leaf primordium, biomass accumulation is controlled mainly by cell proliferation and expansion in the leaves1. However, the ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

The MultiBac Protein Complex Production Platform at the EMBL

Proteomics research revealed the impressive complexity of eukaryotic proteomes in unprecedented detail. It is now a commonly accepted notion that proteins in cells mostly exist not as isolated entities but exert their biological activity in association with many other proteins, in humans ten or more, forming assembly lines in the cell for most if not all vital functions.1,2 Knowledge of the function and architecture of these multiprotein assemblies requires their provision in superior quality and sufficient quantity for detailed analysis. The paucity of many protein complexes in cells, in particular in eukaryotes, prohibits their extraction from native sources, and necessitates recombinant production. The baculovirus expression vector system (BEVS) has proven to be particularly useful for producing eukaryotic proteins, the activity of which often relies on post-translational processing that other commonly used expression systems often cannot support.3 BEVS use a recombinant baculovirus into which the gene of interest was inserted to infect insect cell cultures which in turn produce the protein of choice. MultiBac is a BEVS that has been particularly tailored for the production of eukaryotic protein complexes that contain many subunits.4 A vital prerequisite for efficient production of proteins and their complexes are robust protocols for all steps involved in an expression experiment that ideally can be implemented as standard operating procedures (SOPs) and followed also by non-specialist users with comparative ease. The MultiBac platform at the European Molecular Biology Laboratory (EMBL) uses SOPs for all steps involved in a multiprotein complex expression experiment, starting from insertion of the genes into an engineered baculoviral genome optimized for heterologous protein production properties to small-scale analysis of the protein specimens produced.5-8 The platform is installed in an open-access mode at EMBL Grenoble and has supported many scientists from academia and industry to accelerate protein complex research projects.

Protein complexes catalyze key cellular functions. Detailed functional and structural characterization of many essential complexes requires recombinant production. MultiBac is a baculovirus/insect cell system particularly tailored for expressing eukaryotic proteins and their complexes. MultiBac was implemented as an open-access platform, and standard operating procedures developed to maximize its utility.

Proteomics  research revealed the impressive complexity of eukaryotic proteomes in  unprecedented detail. It is now a commonly accepted notion that ...

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm &#8212; VirusMapper &#8212; packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm ...

Fluorescence-Activated Cell Sorting for the Isolation of Scleractinian Cell Populations

Coral reefs are under threat due to anthropogenic stressors. The biological response of coral to these stressors may occur at a cellular level, but the mechanisms are not well understood. To investigate coral response to stressors, we need tools for analyzing cellular responses. In particular, we need tools that facilitate the application of functional assays to better understand how cell populations are reacting to stress. In the current study, we use fluorescence-activated cell sorting (FACS) to isolate and separate different cell populations in stony corals. This protocol includes: (1) the separation of coral tissues from the skeleton, (2) creation of a single cell suspension, (3) labeling the coral cells using various markers for flow cytometry, and (4) gating and cell sorting strategies. This method will enable researchers to work on corals at the cellular level for analysis, functional assays, and gene expression studies of different cell populations.

Corals create biodiverse ecosystems important for both humans and marine organisms. However, we still do not understand the full potential and function of many coral cells. Here, we present a protocol developed for the isolation, labeling, and separation of stony coral cell populations.

Coral reefs are under threat due to anthropogenic stressors. The biological response of coral to these stressors may occur at a cellular level, but the ...

Assessment of Cellular Bioenergetics in Mouse Hematopoietic Stem and Primitive Progenitor Cells using the Extracellular Flux Analyzer

Under steady state, hematopoietic stem cells (HSCs) remain largely quiescent and are believed to be predominantly reliant on glycolysis to meet their energetic needs. However, under stress conditions such as infection or blood loss, HSCs become proliferative and rapidly produce downstream progenitor cells, which in turn further differentiate, ultimately producing mature blood cells. During this transition and differentiation process, HSCs exit from quiescence and rapidly undergo a metabolic switch from glycolysis to oxidative phosphorylation (OxPHOS). Various stress conditions, such as aging, cancer, diabetes, and obesity, can negatively impact mitochondrial function and thus can alter the metabolic reprogramming and differentiation of HSCs and progenitors during hematopoiesis. Valuable insights into glycolytic and mitochondrial functions of HSCs and progenitors under normal and stress conditions can be gained through the assessment of their extracellular acidification rate (ECAR) and oxygen consumption rate (OCR), which are indicators of cellular glycolysis and mitochondrial respiration, respectively. 
Here, a detailed protocol is provided to measure ECAR and OCR in mouse bone marrow-derived lineage-negative cell populations, which include both hematopoietic stem and primitive progenitor cells (HSPCs), using the extracellular flux analyzer. This protocol describes approaches to isolate lineage-negative cells from mouse bone marrow, explains optimization of cell seeding density and concentrations of 2-deoxy-D-glucose (2-DG, a glucose analog that inhibits glycolysis) and various OxPHOS-targeted drugs (oligomycin, FCCP, rotenone, and antimycin A) used in these assays, and describes drug treatment strategies. Key parameters of glycolytic flux, such as glycolysis, glycolytic capacity, and glycolytic reserve, and OxPHOS parameters, such as basal respiration, maximal respiration, proton leak, ATP production, spare respiratory capacity, and coupling efficiency, can be measured in these assays. This protocol allows ECAR and OCR measurements on non-adherent HSPCs and can be generalized to optimize analysis conditions for any type of suspension cells.

The method presented here summarizes optimized protocols for assessing cellular bioenergetics in non-adherent mouse hematopoietic stem and primitive progenitor cells (HSPCs) using the extracellular flux analyzer to measure the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) of HSPCs in real time.

Under steady state, hematopoietic stem cells (HSCs) remain largely quiescent and are believed to be predominantly reliant on glycolysis to meet their ...

Using Real-Time Cell Metabolic Flux Analyzer to Monitor Osteoblast Bioenergetics

Bone formation by osteoblasts is an essential process for proper bone acquisition and bone turnover to maintain skeletal homeostasis, and ultimately, prevent fracture. In the interest to both optimize peak bone mass and combat various musculoskeletal diseases (i.e., post-menopausal osteoporosis, anorexia nervosa, type 1 and 2 diabetes mellitus), incredible efforts have been made in the field of bone biology to fully characterize osteoblasts throughout their differentiation process. Given the primary role of mature osteoblasts to secrete matrix proteins and mineralization vesicles, it has been noted that these processes take an incredible amount of cellular energy, or adenosine triphosphate (ATP). The overall cellular energy status is often referred to as cellular bioenergetics, and it includes a series of metabolic reactions that sense substrate availability to derive ATP to meet cellular needs. Therefore, the current method details the process of isolating primary, murine bone marrow stromal cells (BMSCs) and monitoring their bioenergetic status using the Real-time cell metabolic flux analyzer at various stages in osteoblast differentiation. Importantly, these data have demonstrated that the metabolic profile changes dramatically throughout osteoblast differentiation. Thus, using this physiologically relevant cell type is required to fully appreciate how a cell's bioenergetic status can regulate the overall function.

Real-time cell metabolic flux assay measures the oxygen consumption rate and extracellular acidification rate, which corresponds to mitochondrial and glycolytic adenosine triphosphate production, using pH and oxygen sensors. The manuscript explains a method to understand the energy status of osteoblasts and the characterization and interpretation of the cellular bioenergetic status.

Bone formation by osteoblasts is an essential process for proper bone acquisition and bone turnover to maintain skeletal homeostasis, and ultimately, ...

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

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A Multi-Parametric Islet Perifusion System within a Microfluidic Perifusion Device

Fluorescence Activated Cell Sorting of Plant Protoplasts

Lipid Vesicle-mediated Affinity Chromatography using Magnetic Activated Cell Sorting (LIMACS): a Novel Method to Analyze Protein-lipid Interaction

Cell Specific Analysis of Arabidopsis Leaves Using Fluorescence Activated Cell Sorting

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

The MultiBac Protein Complex Production Platform at the EMBL

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Fluorescence-Activated Cell Sorting for the Isolation of Scleractinian Cell Populations

Assessment of Cellular Bioenergetics in Mouse Hematopoietic Stem and Primitive Progenitor Cells using the Extracellular Flux Analyzer

Using Real-Time Cell Metabolic Flux Analyzer to Monitor Osteoblast Bioenergetics