We thank David A. Ferrick and David G. Nicholls for contributing to project conception and presentation, Renata L.S. Goncalves and Akos A. Gerencser for data not shown here and for helpful discussions, Barbara Liepe for XF24 consumables, and Andy Neilson for input in developing Eq. (5).

1. Measuring Buffering Power in an Extracellular Flux Instrument: Two Variations
NOTE: the calculations and methods described here were developed using an Extracellular Flux Analyzer. For other instruments, the measurement volume must be scaled appropriately.
<ol>
	<li>Prepare 0.1 M standard HCl in water using HCl concentrate (see Materials and Equipment) according to manufacturer instructions. 
	Note: An example calculation for preparing HCl injections for use in all four injection ports is shown in Table 1:</li>
</ol>
<img alt="Table 1" src="/files/ftp_upload/53464/53464table1.jpg" /> 
Table 1. Consecutive HCl injections into an extracellular flux assay well.
<ol start="2">
	<li>Prepare dilutions of the HCl standard in the medium to be assayed as in Table 1 for the number of technical replicates being carried out in one plate, plus one to allow for pipetting error; e.g., for four technical replicates of the port A injection, prepare a stock of (1.1 &#181;l x 5) 0.1 M HCl in (48.9 &#181;l x 5) assay medium.</li>
	<li>Distribute 50 &#181;l into each Port A of the measurement probe cartridge. Repeat this procedure for remaining ports B, C, and D.</li>
	<li>Run an extracellular flux assay10 with a standard calibration cycle, followed by two cycles of [2 min mix, 1 min wait, and 5 min measurement] for each of the four port additions (see Figure 2).
	<ol>
		<li>Program the experiment above in the instrument software according to software instructions. Load the prepared cartridge into the machine and perform calibration according to software instructions.</li>
		<li>When prompted by the program, remove the calibrant-containing plate and insert the plate containing assay medium in each well into the instrument; continue the program.</li>
	</ol>
	</li>
	<li>Using the average of 8-10 data points obtained at steady state (typically the last 8-10 points) from before and after each port addition, calculate the (cumulative) difference in pH (&#8710;pH) caused by each injection of standard acid.</li>
	<li>Plot &#8710;pH against nmol H+ contained in the 7 &#181;l volume trapped by the measurement probe. The linear slope is the buffering power (BP) in mpH/pmol H+.</li>
	<li>Alternatively to steps 1.2-1.3, carry out the &#8710;pH measurements following an assay in which Ports A, B, and C are used for conducting an experiment, followed by an HCl injection in Port D. As in Table 2, four technical replicates are used to generate each point of a 5-point standard curve in the 20 experimental wells (excluding the four background temperature correction wells) of the extracellular flux assay plate.</li>
</ol>
<img alt="Table 2" src="/files/ftp_upload/53464/53464table2.jpg" /> 
Table 2. Single HCl injection into an extracellular flux assay well.
2. Measuring Buffering Power Using an External pH Meter
NOTE: To measure the buffering power of a medium using an external pH probe, calibrate the probe at 37 &#176;C and maintain this temperature for all reagents during the experiment.
<ol>
	<li>Prepare 0.1 M standard HCl in water using HCl concentrate according to manufacturer instructions.</li>
	<li>Warm pH probe, pH standards, the assay medium whose buffering power is to be measured, and 0.1 M HCl to 37 &#176;C in a water bath.</li>
	<li>Calibrate pH probe at 37 &#176;C according to manufacturer instructions. Maintain all reagents at 37 &#176;C throughout the assay by using a heat plate or water bath.</li>
	<li>Aliquot 10 ml of the assay medium into a small beaker or conical tube. Monitor pH continuously using an immersed pH probe.</li>
	<li>Add 0.1 M HCl to the assay medium in 10-20 &#181;l aliquots.
	<ol>
		<li>Ensure mixing by using a stir bar or by manually swirling the container after each acid addition.</li>
		<li>Allow a few seconds for the pH measurement to stabilize, then record the pH after each addition.</li>
		<li>As demonstrated in Table 3, make a sufficient number of additions to ensure accurate slope calculation and to cover the pH range expected during the experiment.</li>
	</ol>
	</li>
</ol>
<img alt="Table 3" src="/files/ftp_upload/53464/53464table3highres.jpg" width="650" /> 
Table 3. Measuring buffering power using a pH meter. Data represent a typical experiment with six 20 &#181;l additions of 0.1 M HCl.
<ol start="6">
	<li>Plot &#8710;pH against nmol H+ added per 7 &#181;l, giving a linear slope that represents the buffering power (Figure 1).</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/53464/53464fig1.jpg" /> 
Figure 1. Determining buffering power. HCl standard curve measured as in Table 1, Table 2 or (here) as in Table 3. The slope of the linear curve fit gives the buffering power (pH/nmol H+ in 7 &#181;l). Each point represents mean &#177; SEM of n = 9 technical replicates.
<ol start="7">
	<li>Once the buffering power is known through Method 1 or 2 above, convert the ECAR signal (mpH/min) to PPR (pmol H+/min/&#181;g protein) by dividing ECAR by buffering power (BP) (mpH/pmol H+) and scaling to the protein content of each well: 
	PPRtot (pmol H+/min/&#956;g protein) = ECAR (mpH/min)/BP (mpH/pmol H+ in 7 &#956;l)/protein per well (&#956;g)&#160;&#160;&#160;&#160; Equation 6</li>
	<li>Alternatively, use the same experiments in Methods 1 or 2 to calculate the Buffering Capacity (BC) value used by the instrument software to automatically calculate PPR during data collection. 
	NOTE: The instrument user manual12 (page 107) provides detailed information about calculating and using buffering capacity, where BC is described as 
	BC (mol/L) = moles H+/(&#916;pH x buffer volume (L))&#160;&#160;&#160;&#160; Equation 7 
	NOTE: The buffering capacity as defined in Equation 7 can be calculated in the instrument or external pH probe assays described above. Conversion between buffering power and buffering capacity is easily done (see attached spreadsheet): 
	BC = 1 x 10-9/BP ((mpH/pmol H+ in 7 &#181;l) / 7&#160; &#181;l) &#160;&#160;&#160; Equation 8 
	NOTE: If known prior to performing the assay, the buffering capacity can be entered directly into the instrument software during experimental setup.</li>
	<li>Apply this procedure and the calculations used above to most conventional buffer systems, as described in previous publication 6. 
	NOTE: Table 4 lists the buffering power and buffering capacity of several conventional media.</li>
</ol>
<img alt="Table 4" src="/files/ftp_upload/53464/53464table4.jpg" /> 
Table 4. Buffering power and buffering capacity of selected media.
3. Performing an Extracellular Flux Assay Using C2C12 Myoblast Cells
NOTE: In step 3.4.3, there were no observed differences in CO2-derived acid production dependent on the presence of carbonic anhydrase in C2C12 culture, suggesting that its presence is not required for full conversion of CO2 to HCO3- + H+. However, empirically testing this in different experimental systems is recommended before omitting carbonic anhydrase.
<ol>
	<li>Culture mouse C2C12 myoblasts 13 at 37 &#176;C under 95% air/5% CO2 in Dulbecco&#39;s modified Eagle medium (DMEM) with 11.1 mM glucose, 2 mM glutamine, 10% v/v fetal bovine serum (FBS), 100 U/ml penicillin and 100 &#181;g/ml streptomycin.</li>
	<li>24 hr prior to assay, plate/seed cells in 100 &#181;l of the same culture medium at 20,000 cells/well in a 24-well polystyrene extracellular flux assay plate (see Materials and Methods) with no additional coating.</li>
	<li>Dilute oligomycin, FCCP, and rotenone plus myxothiazol, and HCl (optional) to 10x final concentration in Krebs Ringer Phosphate HEPES (KRPH) assay medium (2 mM HEPES, 136 mM NaCl, 2 mM NaH2PO4, 3.7 mM KCl, 1 mM MgCl2, 1.5 mM CaCl2, 0.1% w/v fatty-acid-free bovine serum albumin, pH 7.4 at 37 &#176;C).</li>
	<li>Cell preparation
	<ol>
		<li>30 min prior to the assay, wash adherent cells three times by aspirating to gently remove the medium from the well and then slowly adding 500 &#181;l KRPH.</li>
		<li>Incubate cells after the third wash step at 37 &#176;C under air (not under 5% CO2, which will alter the pH of this bicarbonate-free medium).</li>
		<li>At assay start, replace KRPH in wells with 500 &#181;l fresh KRPH containing 500 U/ml carbonic anhydrase and either glucose (10 mM) or medium only, with no additional substrate.</li>
	</ol>
	</li>
	<li>Loading the sensor cartridge
	<ol>
		<li>Pipet 50 &#181;l aliquots of each 10x compound prepared in Step 3.3 into cartridge ports of an extracellular flux sensor cartridge as follows (final concentrations in assay well given): Port A: 2 &#181;g/ml oligomycin, Port B: 0.5 &#181;M FCCP, Port C: 1 &#181;M rotenone, 1 &#181;M myxothiazol, Port D: HCl (if performing an in-assay acid calibration as described above and in Table 2). 
		NOTE: for the purpose of complete respiratory chain inhibition described here, 1 &#181;M myxothiazol may be used interchangeably with 1 &#181;M antimycin A.</li>
	</ol>
	</li>
	<li>Extracellular flux assay:
	<ol>
		<li>Perform a standard extracellular flux assay for determining respiratory control as described in 10.</li>
	</ol>
	</li>
</ol>
NOTE: For each segment of the experiment, determine the mix, wait, and measurement times desired, as well as the number of cycles per segment.
NOTE: The data in Table 5 were collected over two assay cycles of 2 min mix, 1 min wait, and 5 min measure for each segment, with three assay cycles occurring after the Port D addition of different amounts of HCl (for calibration of buffering power as in Table 2).
<img alt="Table 5" src="/files/ftp_upload/53464/53464table5.jpg" /> 
Table 5. Extracellular flux assay configuration.
4. Measuring End-point Lactate Concentration
NOTE: To validate the indirect assay described here in some different system, end point lactate concentration at the end of an extracellular flux experiment can be determined directly in a conventional 96-well plate by measuring the initial velocity (over 2 min) of reduction of NAD+ &#8594; NADH catalyzed by lactate dehydrogenase, described in detail in our prior publication6. For the data presented in Representative Results, the end point lactate concentration in glucose-containing assay wells was ~40 &#956;M.
<ol>
	<li>Prepare 2x hydrazine medium: 1 M Tris, 20 mM EDTA, 400 mM hydrazine, pH 9.8 at 22 &#176;C). Immediately before assay start, add NAD+ to 4 mM and lactate dehydrogenase (LDH) to 40 U/ml. Final assay medium composition (1x): 500 mM Tris, 10 mM EDTA, 200 mM hydrazine, 2 mM NAD+, 20 U/ml LDH.</li>
	<li>Immediately following the extracellular flux assay, remove 100 &#956;l of assay medium from each well of the extracellular flux assay plate and transfer to a well of an opaque (black) 96-well plate.</li>
	<li>To each sample well, add 100 &#181;l 2x hydrazine medium.</li>
	<li>Immediately load the plate into a microplate reader and begin monitoring NADH fluorescence at 340 nm excitation/460 nm emission.</li>
	<li>Record the initial velocity for approximately 2 min.</li>
	<li>Run a similar experiment to construct a standard curve by plotting initial velocity against lactate concentration for added lactate concentrations from 0 to 50 &#956;M.</li>
	<li>Calculate lactate concentration in each experimental well using the standard curve.</li>
</ol>
5. Measuring Protein Content
<ol>
	<li>Remove remaining assay medium from each well of the assay plate.</li>
	<li>Wash wells three times with 250 &#181;l BSA-free KRPH, being careful to minimize the dislodging of cells from the bottom well surface.</li>
	<li>Add 25 &#181;l RIPA lysis medium (150 mM NaCl, 50 mM Tris, 1 mM EGTA, 1 mM EDTA, 1% v/v Triton X-100, 0.5% w/v sodium deoxycholate, 0.1% v/v SDS, pH 7.4 at 22 &#176;C) to each well of the assay plate.</li>
	<li>Incubate plate on ice for 30 min.</li>
	<li>Agitate plate on a plate shaker at 1,200 rpm for 5 min.</li>
	<li>Measure protein concentration by standard methods, e.g., by BCA assay, ensuring that the lysis buffer composition is compatible with the measurement method. The protein content in the experiment in Figure 2 was ~4 &#181;g/well.</li>
</ol>

<ol>
	<li>Brand, M. D., Nicholls, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+mitochondrial+dysfunction+in+cells.">Assessing mitochondrial dysfunction in cells.</a> Biochem. J. 435 (2), 297-312 (2011).</li><li>Gerencser, A. 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=Quantitative+microplate-based+respirometry+with+correction+for+oxygen+diffusion.">Quantitative microplate-based respirometry with correction for oxygen diffusion.</a> Anal. Chem. 81 (16), 6868-6878 (2009).</li><li>Divakaruni, A. S., Paradyse, A., Ferrick, D. A., Murphy, A. N., Jastroch, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+and+interpretation+of+microplate-based+oxygen+consumption+and+pH+data.">Analysis and interpretation of microplate-based oxygen consumption and pH data.</a> Meth. Enzymol. 547, 309-354 (2014).</li><li>Renner, K., Jansen-D&#252;rr, P., Gnaiger, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biphasic+oxygen+kinetics+of+cellular+respiration+and+linear+oxygen+dependence+of+antimycin+A+inhibited+oxygen+consumption.">Biphasic oxygen kinetics of cellular respiration and linear oxygen dependence of antimycin A inhibited oxygen consumption.</a> Mol. Biol. Rep. 29 (1-2), 83-87 (2002).</li><li>Helmlinger, G., Sckell, A., Dellian, M., Forbes, N. S., Jain, R. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acid+production+in+glycolysis-impaired+tumors+provides+new+insights+into+tumor+metabolism.">Acid production in glycolysis-impaired tumors provides new insights into tumor metabolism.</a> Clin. Cancer Res. 8 (4), 1284-1291 (2002).</li><li>Mookerjee, S. A., Goncalves, R. L. S., Gerencser, A. G., Nicholls, D. G., Brand, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+contributions+of+respiration+and+glycolysis+to+extracellular+acid+production.">The contributions of respiration and glycolysis to extracellular acid production.</a> Biochim. Biophys. Acta. 1847, 171-181 (2015).</li><li>Wu, 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=Multiparameter+metabolic+analysis+reveals+a+close+link+between+attenuated+mitochondrial+bioenergetic+function+and+enhanced+glycolysis+dependency+in+human+tumor+cells.">Multiparameter metabolic analysis reveals a close link between attenuated mitochondrial bioenergetic function and enhanced glycolysis dependency in human tumor cells.</a> Am. J. Physiol. 292 (1), C125-C136 (2006).</li><li>Nadanaciva, 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=Assessment+of+drug-induced+mitochondrial+dysfunction+via+altered+cellular+respiration+and+acidification+measured+in+a+96-well+platform.">Assessment of drug-induced mitochondrial dysfunction via altered cellular respiration and acidification measured in a 96-well platform.</a> J. Bioenerg. Biomembr. 44 (4), 421-437 (2012).</li><li>Pelletier, M., Billingham, L. K., Ramaswamy, M., Siegel, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extracellular+flux+analysis+to+monitor+glycolytic+rates+and+mitochondrial+oxygen+consumption.">Extracellular flux analysis to monitor glycolytic rates and mitochondrial oxygen consumption.</a> Meth. Enzymol. 542, 125-149 (2014).</li><li>Nicholls, D. G., Darley-Usmar, V. M., Wu, M., Jensen, P. B., Rogers, G. W., Ferrick, 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=Bioenergetic+profile+experiment+using+C2C12+myoblast+cells.">Bioenergetic profile experiment using C2C12 myoblast cells.</a> J. Vis. Exp. (46), 2511 (2010).</li><li>Rogers, G. W., 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=High+throughput+microplate+respiratory+measurements+using+minimal+quantities+of+isolated+mitochondria.">High throughput microplate respiratory measurements using minimal quantities of isolated mitochondria.</a> PLoS ONE. 6 (7), e21746 (2011).</li><li>Seahorse Biosciences. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> F24 Extracellular Flux Analyzer and Prep Station Installation and Operation Manual. 1.7, (2010).</li><li>Blau, H., Chiu, C., Webster, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytoplasmic+activation+of+human+nuclear+genes+in+human+heterocaryons.">Cytoplasmic activation of human nuclear genes in human heterocaryons.</a> Cell. 32, 1171-1180 (1983).</li></ol>

Dr. Shona Mookerjee declares that she has no competing financial interests. Dr. Martin Brand has consulted for Seahorse Biosciences, which produces instruments and reagents used in this Article.

Extracellular acidification is an easily measured indication of cellular metabolic rate. To properly determine the rate of cellular glycolysis (as defined by lactate production) it is critical to know the buffering power of the assay medium, and to convert the extracellular flux measurements of oxygen consumption and acidification to proton production rates. By performing this calculation, the acidification resulting from CO2 released in the citric acid cycle can be subtracted, leaving the acidification that results from lactate production.
The multiple different ways given here to measure buffering power for this correction carry different advantages and disadvantages. External measurement using a pH probe is highly accurate and reproducible, but may not reflect small differences in pH detection introduced by the fluorophores contained within the assay plate, the addition of compounds during the assay, or the presence of the cells themselves. The in-plate pH measurements address these issues, but also introduce varying degrees of experimental noise.
The CO2 correction to ECAR allows for the first time the unambiguous and quantitative calculation of glycolytic rate, and reveals variation in respiratory and glycolytic contribution to total ECAR during the course of an experiment. Using Equation 5 and the measurements of OCR, ECAR, and buffering power, glycolytic rate can be calculated using the simple spreadsheet provided (Table 6). This rate can be verified by post-hoc lactate measurement if desired 6. In cells where the pentose phosphate pathway is highly active, the use of pathway inhibitors such as 6-aminonicotinamide may be useful to isolate glycolytic rate. Calculation of the contributions of both CO2- and lactate-derived H+ from the total measured Extra Cellular Acidification Rate and Oxygen Consumption Rate is an invaluable tool for using extracellular flux data to make powerful and quantitative statements about metabolic activity.
Using the procedures described here, including various modifications for measuring buffering power, and optimizing the extracellular flux experiment for the cells under investigation and data desired, the rate of glycolysis in intact cells can be quantified under a wide range of experimental conditions. This method is limited to cells that can grow in adherent culture on (or cells or organelles that can be adhered to) a polystyrene surface. It is most reliable when cultured cells are homogenous and confluent, though useful data may still be obtained over a range of these conditions. The calculations require some knowledge of the metabolism of the cells, as max H+/O2 ranges from 0.65 to 1.0 for full oxidation of different substrates and more for partial oxidation 6, however, if the cells are known to oxidize glucose, a value of 1.0 can be assumed.
Though relevant to all metabolic characterization, this method may be particularly helpful when used in systems in which a shift between respiratory and glycolytic metabolism to maintain cellular ATP supply is a critical phenotype, including the characterization of stem cells and tumor-derived cancer cells. Understanding metabolic control alterations in these and other contexts will allow a greater degree of sophistication and accuracy in the experimental design and analysis of these cell types.

The overall goal of this method is to accurately measure the glycolytic rate of cells using extracellular flux analysis. Quantitative measurement of glycolytic rate using extracellular acidification is the desired endpoint of many experiments. However, the total rate of extracellular acidification is the sum of two components: respiratory acidification, in the form of CO2 (which hydrates to H2CO3 then dissociates to HCO3- + H+), and glycolytic acidification, in the form of lactate- + H+.
The contributions of CO2 to total extracellular acidification have until recently been considered negligible in the measurement platform used here, the XF24 analyzer 7. However, it is clear in multiple other systems that CO2 can be a major contributor to extracellular acidification4-5. Multiple papers acknowledge this contribution, but do not attempt direct quantitation of CO2-derived acid 3,8,9. We recently demonstrated quantitatively that CO2 production is a significant source of extracellular acidification in this system 6. Moreover, though there are multiple metabolic pathways that generate CO2 from glucose catabolism, those carried out by matrix dehydrogenases in the citric acid cycle are the overwhelming contributors and all other sources generate amounts of CO2 that are within experimental error 6.
Without correcting for CO2 production, extracellular acidification is therefore an ambiguous indicator of glycolytic rate and cannot be used quantitatively. Our previous publication highlights several instances where respiratory CO2 comprises the bulk of the total acidification signal, even in cells generally believed to primarily use glycolysis6. Additionally, the respiratory CO2 contribution to total acidification varies widely during the course of common metabolic profiling experiments, demonstrating that correct comparison of the glycolytic rate during different parts of an experiment requires correction for CO2.
To measure the glycolytic rate of cells using the rate of extracellular acidification, it is necessary to convert pH changes to changes in total H+ generated, and to subtract the extracellular acidification caused by CO2 released during operation of the citric acid cycle. Here, we describe a straightforward method for measuring extracellular proton production rate (from extracellular changes in pH and the calibrated buffering power of the assay medium) and CO2 production (from extracellular changes in O2 concentration), and demonstrate how to calculate glycolytic rate using these measurements.
This method strengthens the utility of extracellular acidification measurement by using it to properly calculate glycolytic rate as defined by lactate production. Without correction for respiratory CO2 (or direct measurement of lactate), it is impossible to determine if and to what extent the total acidification rate reflects glycolytic rate, confounding the interpretation of experiments that use total extracellular acidification as a direct measurement of lactate production.
CALCULATIONS
CO2 and lactate are, within experimental error, the only two contributors to extracellular acid production, based on experiments with myoblast cells6. Therefore, the rate of total extracellular acidification (PPR, proton production rate) can be defined as:
PPRtot = PPRresp + PPRglyc &#160; &#160; Equation 1&#160;
where tot = total; resp = respiratory; glyc = glycolytic. Glycolytic PPR is thus:
PPRglyc = PPRtot - PPRresp &#160; &#160; Equation 2
Here,
PPRtot = ECARtot/BP &#160; &#160; Equation 3
where ECAR = extracellular acidification rate (mpH/min), and BP = buffering power (mpH/pmol H+ in 7 &#181;l), while
PPRresp = (10pH-pK1/(1+10pH-pK1))(max H+/O2)(OCRtot &#8211; OCRrot/myx) &#160; &#160; Equation 4
where K1 = combined equilibrium constant of CO2 hydration and dissociation to HCO3- + H+; max H+/O2 = the CO2-derived acidification for a particular metabolic transformation such as complete oxidation of glucose6; OCR = oxygen consumption rate (pmol O2/min), and OCRrot/myx = non-mitochondrial OCR.
Equation 4 isolates mitochondrial OCR by subtracting any non-mitochondrial OCR (defined as OCR that is resistant to the mitochondrial respiratory poisons rotenone and myxothiazol) and accounts for the maximum H+ generated per O2 consumed for each substrate (max H+/O2) (see 6), as well as the proportion of CO2 giving rise to H+ at the experimental temperature and pH (10pH-pK1/(1+10pH-pK1). For full oxidation of glucose, mitochondrial Oxygen Consumption Rate (OCR) is exactly equal to the rate of CO2 production. In the confined assay volume of extracellular flux measurement, CO2 produced by respiration remains trapped in the assay medium. Most of the trapped CO2 is hydrated to H2CO3, which then dissociates to HCO3- + H+. A small fraction remains dissolved but not hydrated, and another small fraction is hydrated but not dissociated, as dictated thermodynamically by the combined equilibrium constant of CO2 hydration and dissociation to HCO3- + H+ at experimental temperature (37 &#176;C) and pH (~7.4).
Thus, the complete equation for calculating PPRg by subtracting PPRresp from PPRtot is:
PPRglyc = ECARtot/BP &#8211; (10pH-pK1/(1+10pH-pK1))(max H+/O2)(OCRtot &#8211; OCRrot/myx)&#160;&#160;&#160;&#160; Equation 5
In this way, rates of respiration and glycolysis, as well as their associated ATP production rates, can be quantitatively determined from straightforward measurements (oxygen consumption, extracellular acidification, buffering capacity) and import or calculation of other required values (H+/O2, P/O, and the equilibrium constant K1) 6. The experiment described here expands on standard techniques for using the Extracellular Flux Analyzer such as Seahorse XF24 10,11; for other extracellular flux measurement formats (e.g., XFe96, or XFp), all volumes below should be scaled appropriately.
The buffering power of the assay medium can be measured by construction of a standard curve either directly in the extracellular flux platform or separately using a calibrated pH probe. Here, three options for measuring buffering by the extracellular flux assay medium are given, including using all injection ports of the extracellular flux analyzer with cell-free sample wells, or using only the last injection port in cell-containing wells (section 1) or by using an external pH measurement (section 2). See the attached spreadsheet for the full calculations of example data.
To measure buffering power using the pH-detecting capability of the extracellular flux instrument, it is safest to use cell-free wells to minimize signal variation. However, within the error, no statistical difference exists between cell-free and cell-containing wells when performing this measurement (data not shown). NOTE: The variation described in step 1.7 carries the advantage of accounting for any potential changes to buffering conferred by added compounds or by the presence of cells, with the disadvantage of noisier signal. However, as stated above, no significant differences were found in the calculated buffering power between the cell-free design shown in Table 1 and the post-experiment design in Table 2 under the experimental conditions described here.
Additionally, over small &#916;pH ranges (&#60;0.4 units; experimentally best restricted to 0.2 units), the linear slope obtained by plotting &#916; mpH/pmol H+ adequately approximates the logarithmic relationship between &#916;pH and [H+]. The slope of this standard curve therefore represents the buffering power of the assay medium under test in pH/nmol H+ in 7 &#181;l, or mpH/pmol H+ in 7 &#181;l. We recommend increasing medium buffering power or decreasing cell density for samples that exceed a 0.2 pH unit change during the measurement time. The measurement time may also be decreased, but this may shorten the steady state acidification rate and introduce error into the rate calculation.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Pherastar FS</td>
			<td>BMG</td>
			<td>n/a</td>
			<td>microplate reader</td>
		</tr>
		<tr>
			<td>Seahorse XF-24</td>
			<td>Seahorse Bioscience</td>
			<td>n/a</td>
			<td>extracellular flux instrument</td>
		</tr>
		<tr>
			<td>Seahorse XF assay plate</td>
			<td>Seahorse Bioscience</td>
			<td>V7-PS</td>
			<td>consumable</td>
		</tr>
		<tr>
			<td>XF Calibrant</td>
			<td>Seahorse Bioscience</td>
			<td>100840-000</td>
			<td>solution</td>
		</tr>
		<tr>
			<td>HCl standard</td>
			<td>Sigma</td>
			<td>38280</td>
			<td>chemical</td>
		</tr>
		<tr>
			<td>oligomycin</td>
			<td>Sigma</td>
			<td>O4876</td>
			<td>chemical</td>
		</tr>
		<tr>
			<td>FCCP</td>
			<td>Sigma</td>
			<td>C2920</td>
			<td>chemical</td>
		</tr>
		<tr>
			<td>Rotenone</td>
			<td>Sigma</td>
			<td>R8875</td>
			<td>chemical</td>
		</tr>
		<tr>
			<td>Myxothiazol</td>
			<td>Sigma</td>
			<td>T5580</td>
			<td>chemical</td>
		</tr>
		<tr>
			<td>DMEM</td>
			<td>Corning</td>
			<td>10-013-CV</td>
			<td>medium component</td>
		</tr>
		<tr>
			<td>FBS</td>
			<td>Corning</td>
			<td>35-010-CV</td>
			<td>medium component</td>
		</tr>
		<tr>
			<td>penicillin/streptomycin</td>
			<td>Corning</td>
			<td>30-002-CI</td>
			<td>medium component</td>
		</tr>
		<tr>
			<td>carbonic anhydrase</td>
			<td>Sigma</td>
			<td>C2624</td>
			<td>chemical</td>
		</tr>
		<tr>
			<td>96-well assay plate</td>
			<td>Corning</td>
			<td>CLS3991</td>
			<td>consumable</td>
		</tr>
		<tr>
			<td>NAD+</td>
			<td>Sigma</td>
			<td>N7004</td>
			<td>chemical</td>
		</tr>
		<tr>
			<td>LDH</td>
			<td>Sigma</td>
			<td>L1254</td>
			<td>chemical</td>
		</tr>
	</tbody>
</table>

measurement and analysis of extracellular acid production to determine glycolytic rate

Extracellular measurement of oxygen consumption and acid production is a simple and powerful way to monitor rates of respiration and glycolysis1. Both mitochondrial (respiration) and non-mitochondrial (other redox) reactions consume oxygen, but these reactions can be easily distinguished by chemical inhibition of mitochondrial respiration. However, while mitochondrial oxygen consumption is an unambiguous and direct measurement of respiration rate2, the same is not true for extracellular acid production and its relationship to glycolytic rate 3-6. Extracellular acid produced by cells is derived from both lactate, produced by anaerobic glycolysis, and CO2, produced in the citric acid cycle during respiration. For glycolysis, the conversion of glucose to lactate- + H+ and the export of products into the assay medium is the source of glycolytic acidification. For respiration, the export of CO2, hydration to H2CO3 and dissociation to HCO3- + H+ is the source of respiratory acidification. The proportions of glycolytic and respiratory acidification depend on the experimental conditions, including cell type and substrate(s) provided, and can range from nearly 100% glycolytic acidification to nearly 100% respiratory acidification 6. Here, we demonstrate the data collection and calculation methods needed to determine respiratory and glycolytic contributions to total extracellular acidification by whole cells in culture using C2C12 myoblast cells as a model.

Figure 2 shows the raw data for a typical experiment. The last 10 measurement points from the point-to-point recording of both OCR and pH (shaded vertical bars) were used for the calculations. Initial concerns that the average value (middle point measurement) of each assay cycle would not provide sufficient resolution of rate for an accurate calculation, particularly as there appeared to be a slight lag between port addition and steady state acidification rate, were not borne out, as this does not appear to contribute significantly to calculation error (not shown). Alternatively, if the correct buffering capacity is entered during experimental setup, PPR can be read directly from the instrument data collection readout by displaying the PPR output in the instrument software or in the PC-compatible format available as one of the data output settings.
<img alt="Figure 2" src="/files/ftp_upload/53464/53464fig2.jpg" /> 
Figure 2. Representative extracellular flux traces of O2 and H+. OCR and pH traces for the example experiment in Table 5, containing 10 mM glucose at assay start. Port D had different HCl concentrations for calibration of buffering power (not shown in these averaged traces). Data from previous publication6. Each point represents mean &#177; SEM of n = 8 biological replicates. <a href="https://www.jove.com/files/ftp_upload/53464/53464fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Data analysis of representative results
Using the spreadsheet shown in Table 6 and provided as an attachment, data values from individual wells may be entered in the columns shown with yellow headers. All six columns to the right are calculated from these entries. The example in Table 6 shows the calculations of PPRresp and PPRglyc using ECAR and OCR data from individual wells for the native conditions with or without added glucose, prior to Port A addition of oligomycin. Technical replicates on each biological preparation are normally averaged to give single values of the outputs in the last four columns, then data from different biological preparations are averaged with appropriate propagation of error statistics in BP and these four values.
<img alt="Table 6" src="/files/ftp_upload/53464/53464table6.jpg" /> 
Table 6. Calculation of respiratory and glycolytic acidification. Columns headed in yellow indicate values to be entered from calculation (e.g., BP, max H+/O2), or from data collection (e.g., ECARtot, OCR). <a href="https://www.jove.com/files/ftp_upload/53464/53464table6.jpg" target="_blank">Please click here to view a larger version of this table.</a> | <a href="https://www.jove.com/files/ftp_upload/53464/53464_Table_6.xlsx">Please click here to download this table as an Excel spreadsheet.</a>
Contributions of glycolysis and respiration to PPR after correction
Figure 3 shows the graphical output of data calculated as in Table 6 for native rates of glycolytic and respiratory acidification, rates following oligomycin addition (Port A), and rates following FCCP addition (Port B). These data clearly demonstrate how the proportions of respiratory and glycolytic acidification change with choice of substrate (glucose vs. control (ctl) with none added) and with mitochondrial status (native function vs. pharmacologically altered function).
<img alt="Figure 3" src="/files/ftp_upload/53464/53464fig3.jpg" /> 
Figure 3. Proton production rate (PPR) from glycolytic and respiratory sources. PPR from respiration (open portions) and glycolysis (filled portions) of C2C12 cells calculated using Equation 5 with added glucose (three left bars) or without added glucose (three right bars). Data from6. All data represent mean &#177; SEM of n = 8 biological replicates.

Watch this Scientific Journal Video about Measurement and Analysis of Extracellular Acid Production to Determine Glycolytic Rate at JoVE.com

Measurement and Analysis of Extracellular Acid Production to Determine Glycolytic Rate

Glycolysis is a defining metabolic marker in multiple biological systems. Monitoring glycolysis by measuring the extracellular flux of H+ is common, but requires correction to be quantitative and unambiguous. Here, we demonstrate how to gather and correct extracellular flux data to distinguish between respiratory and glycolytic sources of extracellular acidification.

Extracellular measurement of oxygen consumption and acid production is a simple and powerful way to monitor rates of respiration and glycolysis1. Both ...

measurement-analysis-extracellular-acid-production-to-determine

Research

JoVE Journal

Biology

24.9K Views.  Touro University California College of Pharmacy. Glycolysis is a defining metabolic marker in multiple biological systems. Monitoring glycolysis by measuring the extracellular flux of H+ is common, but requires correction to be quantitative and unambiguous. Here, we demonstrate how to gather and correct extracellular flux data to distinguish between respiratory and glycolytic sources of extracellular acidification.

Video: Measurement and Analysis of Extracellular Acid Production to Determine Glycolytic Rate

Quantitative Measurement of Invadopodia-mediated Extracellular Matrix Proteolysis in Single and Multicellular Contexts

Cellular invasion into local tissues is a process important in development and homeostasis. Malregulated invasion and subsequent cell movement is characteristic of multiple pathological processes, including inflammation, cardiovascular disease and tumor cell metastasis1. Focalized proteolytic degradation of extracellular matrix (ECM) components in the epithelial or endothelial basement membrane is a critical step in initiating cellular invasion. In tumor cells, extensive in vitro analysis has determined that ECM degradation is accomplished by ventral actin-rich membrane protrusive structures termed invadopodia2,3. Invadopodia form in close apposition to the ECM, where they moderate ECM breakdown through the action of matrix metalloproteinases (MMPs). The ability of tumor cells to form invadopodia directly correlates with the ability to invade into local stroma and associated vascular components3. 
Visualization of invadopodia-mediated ECM degradation of cells by fluorescent microscopy using dye-labeled matrix proteins coated onto glass coverslips has emerged as the most prevalent technique for evaluating the degree of matrix proteolysis and cellular invasive potential4,5. Here we describe a version of the standard method for generating fluorescently-labeled glass coverslips utilizing a commercially available Oregon Green-488 gelatin conjugate. This method is easily scaled to rapidly produce large numbers of coated coverslips. We show some of the common microscopic artifacts that are often encountered during this procedure and how these can be avoided. Finally, we describe standardized methods using readily available computer software to allow quantification of labeled gelatin matrix degradation mediated by individual cells and by entire cellular populations. The described procedures provide the ability to accurately and reproducibly monitor invadopodia activity, and can also serve as a platform for evaluating the efficacy of modulating protein expression or testing of anti-invasive compounds on extracellular matrix degradation in single and multicellular settings.

We describe the prototypical method for producing microscope coverslips coated with fluorescent gelatin for visualizing invadopodia-mediated matrix degradation. Computational techniques using available software are presented for quantifying the resultant levels of matrix proteolysis by single cells within a mixed population and for multicellular groups encompassing entire microscopic fields.

Cellular invasion into local tissues is a process important in development and homeostasis.  Malregulated invasion and subsequent cell movement is ...

A High-throughput Method for Measurement of Glomerular Filtration Rate in Conscious Mice

The measurement of glomerular filtration rate (GFR) is the gold standard in kidney function assessment. Currently, investigators determine GFR by measuring the level of the endogenous biomarker creatinine or exogenously applied radioactive labeled inulin (3H or 14C). Creatinine has the substantial drawback that proximal tubular secretion accounts for ~50% of total renal creatinine excretion and therefore creatinine is not a reliable GFR marker. Depending on the experiment performed, inulin clearance can be determined by an intravenous single bolus injection or continuous infusion (intravenous or osmotic minipump). Both approaches require the collection of plasma or plasma and urine, respectively. Other drawbacks of radioactive labeled inulin include usage of isotopes, time consuming surgical preparation of the animals, and the requirement of a terminal experiment. Here we describe a method which uses a single bolus injection of fluorescein isothiocyanate-(FITC) labeled inulin and the measurement of its fluorescence in 1-2 &mu;l of diluted plasma. By applying a two-compartment model, with 8 blood collections per mouse, it is possible to measure GFR in up to 24 mice per day using a special work-flow protocol. This method only requires brief isoflurane anesthesia with all the blood samples being collected in a non-restrained and awake mouse. Another advantage is that it is possible to follow mice over a period of several months and treatments (i.e. doing paired experiments with dietary changes or drug applications). We hope that this technique of measuring GFR is useful to other investigators studying mouse kidney function and will replace less accurate methods of estimating kidney function, such as plasma creatinine and blood urea nitrogen.

Measurement of glomerular filtration rate (GFR) is the gold standard for kidney function assessment. Here we describe a high-throughput method which allows the determination of GFR in conscious mice by using a single bolus injection, determination of fluorescein isothiocyanate (FITC)-inulin in plasma and calculation of GFR by a two-phase exponential decay model.

The measurement of  glomerular filtration rate (GFR) is the gold standard in kidney function  assessment. Currently, investigators determine GFR by ...

Measurement of Metabolic Rate in Drosophila using Respirometry

Metabolic disorders are a frequent problem affecting human health. Therefore, understanding the mechanisms that regulate metabolism is a crucial scientific task. Many disease causing genes in humans have a fly homologue, making Drosophila a good model to study signaling pathways involved in the development of different disorders. Additionally, the tractability of Drosophila simplifies genetic screens to aid in identifying novel therapeutic targets that may regulate metabolism. In order to perform such a screen a simple and fast method to identify changes in the metabolic state of flies is necessary. In general, carbon dioxide production is a good indicator of substrate oxidation and energy expenditure providing information about metabolic state. In this protocol we introduce a simple method to measure CO2 output from flies. This technique can potentially aid in the identification of genetic perturbations affecting metabolic rate.

Measurement of Metabolic Rate in Drosophila using Respirometry

Metabolic disorders are among one of the most common diseases in humans. The genetically tractable model organism D. melanogaster can be used to identify novel genes that regulate metabolism. This paper describes a relatively simple method which allows studying the metabolic rate in flies by measuring their CO2 production.

Metabolic disorders are a frequent problem affecting human health. Therefore, understanding the mechanisms that regulate metabolism is a crucial ...

Measurement of Extracellular Ion Fluxes Using the Ion-selective Self-referencing Microelectrode Technique

Cells from animals, plants and single cells are enclosed by a barrier called the cell membrane that separates the cytoplasm from the outside. Cell layers such as epithelia also form a barrier that separates the inside from the outside or different compartments of multicellular organisms. A key feature of these barriers is the differential distribution of ions across cell membranes or cell layers. Two properties allow this distribution: 1) membranes and epithelia display selective permeability to specific ions; 2) ions are transported through pumps across cell membranes and cell layers. These properties play crucial roles in maintaining tissue physiology and act as signaling cues after damage, during repair, or under pathological condition. The ion-selective self-referencing microelectrode allows measurements of specific fluxes of ions such as calcium, potassium or sodium at single cell and tissue levels. The microelectrode contains an ionophore cocktail which is selectively permeable to a specific ion. The internal filling solution contains a set concentration of the ion of interest. The electric potential of the microelectrode is determined by the outside concentration of the ion. As the ion concentration varies, the potential of the microelectrode changes as a function of the log of the ion activity. When moved back and forth near a source or sink of the ion (i.e. in a concentration gradient due to ion flux) the microelectrode potential fluctuates at an amplitude proportional to the ion flux/gradient. The amplifier amplifies the microelectrode signal and the output is recorded on computer. The ion flux can then be calculated by Fick&#8217;s law of diffusion using the electrode potential fluctuation, the excursion of microelectrode, and other parameters such as the specific ion mobility. In this paper, we describe in detail the methodology to measure extracellular ion fluxes using the ion-selective self-referencing microelectrode and present some representative results.

Transporters in cell membranes allow differential segregation of ions across cell membranes or cell layers and play crucial roles during tissue physiology, repair and pathology. We describe the ion-selective self-referencing microelectrode that allows the measurement of specific ion fluxes at single cells and tissues in vivo.

Cells from animals, plants and single cells are enclosed by a barrier called the cell membrane that separates the cytoplasm from the outside. Cell layers ...

Measurement of Carbon Dioxide Production from Radiolabeled Substrates in Drosophila melanogaster

The power of Drosophila genetics is increasingly being applied to questions of hormone signaling and metabolism and to the development of models of human disease in this organism. Sensitive methods for measurements of parameters such as metabolic rates are needed to drive the understanding of physiology and disease in small animals such as the fruit fly. The method described here assesses fuel oxidation in small numbers of adult flies fed food containing trace amounts of 14C-labeled substrates such as glucose or fatty acid. After the feeding period and any additional experimental manipulations, flies are transferred to short tubes capped with mesh, which are then placed in glass vials containing KOH-saturated filter paper that traps exhaled, radiolabeled CO2 generated from oxidation of radiolabeled substrates as potassium bicarbonate, KHCO3. This radiolabeled bicarbonate is measured by scintillation counting. This is a quantitative, reproducible, and simple approach for the study of fuel oxidation. The use of radiolabeled glucose, fatty acids, or amino acids allows determination of the contribution of these different fuel sources to energy metabolism under different conditions such as feeding and fasting and in different genetic backgrounds. This complements other approaches used to measure in vivo energy metabolism and should further the understanding of metabolic regulation.

Measurement of Carbon Dioxide Production from Radiolabeled Substrates in Drosophila melanogaster

This paper describes a method for the measurement of fuel oxidation in Drosophila melanogaster in which trace amounts of specific radiolabeled metabolic substrates are fed to flies. The exhaled radiolabeled CO2 that is a produced from fuel oxidation is collected and measured.

The power of Drosophila genetics is increasingly being applied to questions of hormone signaling and metabolism and to the development of models of human ...

Measurement of Energy Metabolism in Explanted Retinal Tissue Using Extracellular Flux Analysis

High acuity vision is a heavily energy-consuming process, and the retina has developed several unique adaptations to precisely meet such demands while maintaining transparency of the visual axis. Perturbations to this delicate balance cause blinding illnesses, such as diabetic retinopathy. Therefore, the understanding of energy metabolism changes in the retina during disease is imperative to the development of rational therapies for various causes of vison loss. The recent advent of commercially-available extracellular flux analyzers has made the study of retinal energy metabolism more accessible. This protocol describes the use of such an analyzer to measure contributions to retinal energy supply through its two principle arms - oxidative phosphorylation and glycolysis - by quantifying changes in oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) as proxies for these pathways. This technique is readily performed in explanted retinal tissue, facilitating assessment of responses to multiple pharmacologic agents in a single experiment. Metabolic signatures in retinas from animals lacking rod photoreceptor signaling are compared to wild-type controls using this method. A major limitation in this technique is the lack of ability to discriminate between light-adapted and dark-adapted energy utilization, an important physiologic consideration in retinal tissue.

This technique describes real time recording of oxygen consumption and extracellular acidification rates in explanted mouse retinal tissues using an extracellular flux analyzer.

High acuity vision is a heavily energy-consuming process, and the retina has developed several unique adaptations to precisely meet such demands while ...

Flow Cytometric Analysis of Extracellular Vesicles from Cell-conditioned Media

Flow cytometry (FC) is the method of choice for semi-quantitative measurement of cell-surface antigen markers. Recently, this technique has been used for phenotypic analyses of extracellular vesicles (EV) including exosomes (Exo) in the peripheral blood and other body fluids. The small size of EV mandates the use of dedicated instruments having a detection threshold around 50-100 nm. Alternatively, EV can be bound to latex microbeads that can be detected by FC. Microbeads, conjugated with antibodies that recognize EV-associated markers/Cluster of Differentiation CD63, CD9, and CD81 can be used for EV capture. Exo isolated from CM can be analyzed with or without pre-enrichment by ultracentrifugation. This approach is suitable for EV analyses using conventional FC instruments. Our results demonstrate a linear correlation between Mean Fluorescence Intensity (MFI) values and EV concentration. Disrupting EV through sonication dramatically decreased MFI, indicating that the method does not detect membrane debris. We report an accurate and reliable method for the analysis of EV surface antigens, which can be easily implemented in any laboratory.

The protocol describes a reproducible method designed for use with cell culture supernatants to detect surface epitopes on small extracellular vesicles (EV). It utilizes specific EV immunoprecipitation using beads coupled with antibodies that recognize surface antigen CD9, CD63, and CD81. The method is optimized for downstream flow cytometry analysis.

Flow cytometry (FC) is the method of choice for semi-quantitative measurement of cell-surface antigen markers. Recently, this technique has been used for ...

Purification and Analysis of Caenorhabditis elegans Extracellular Vesicles

The secretion of small membrane-bound vesicles into the external environment is a fundamental physiological process of all cells. These extracellular vesicles (EVs) function outside the cell to regulate global physiological processes by transferring proteins, nucleic acids, metabolites, and lipids between tissues. EVs reflect the physiological state of their cells of origin. EVs are implicated to have fundamental roles in virtually every aspect of human health. Thus, EV protein and genetic cargos are being increasingly analyzed for biomarkers of health and disease. However, the EV field still lacks a tractable invertebrate model system that permits the study of EV cargo composition. C. elegans is well suited for EV research because it actively secretes EVs outside of its body into its external environment, permitting facile isolation. This article provides all the necessary information for generating, purifying, and quantifying these environmentally secreted C. elegans EVs including how to work quantitatively with very large populations of age-synchronized worms, purifying EVs, and a flow cytometry protocol that directly measures the number of intact EVs in the purified sample. Thus, the large library of genetic reagents available for C. elegans research can be tapped into for investigating the impacts of genetic pathways and physiological processes on EV cargo composition.

Purification and Analysis of Caenorhabditis elegans Extracellular Vesicles

This article presents methods for generating, purifying, and quantifying Caenorhabditis elegans extracellular vesicles.

The secretion of small membrane-bound vesicles into the external environment is a fundamental physiological process of all cells. These extracellular ...

Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles

The physiological and pathophysiological roles of extracellular vesicles (EVs) have become increasingly recognized, making the EV field a quickly evolving area of research. There are many different methods for EV isolation, each with distinct advantages and disadvantages that affect the downstream yield and purity of EVs. Thus, characterizing the EV prep isolated from a given source by a chosen method is important for interpretation of downstream results and comparison of results across laboratories. Various methods exist for determining the size and quantity of EVs, which can be altered by disease states or in response to external conditions. Nanoparticle tracking analysis (NTA) is one of the prominent technologies used for high-throughput analysis of individual EVs. Here, we present a detailed protocol for quantification and size determination of EVs isolated from mouse perigonadal adipose tissue and human plasma using a breakthrough technology for NTA representing major advances in the field. The results demonstrate that this method can deliver reproducible and valid total particle concentration and size distribution data for EVs isolated from different sources using different methods, as confirmed by transmission electron microscopy. The adaptation of this instrument for NTA will address the need for standardization in NTA methods to increase rigor and reproducibility in EV research.

We demonstrate how to use a novel nanoparticle tracking analysis instrument to estimate the size distribution and total particle concentration of extracellular vesicles isolated from mouse perigonadal adipose tissue and human plasma.

The physiological and pathophysiological roles of extracellular vesicles (EVs) have become increasingly recognized, making the EV field a quickly evolving ...

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software

It is well established that colony growth of filamentous fungi, mostly dependent on changes in hyphae/mycelia apical growth rate, is macroscopically estimated on solidified media by comparing colony size. However, to quantitatively measure the growth rate of genetically different fungal strains or strains under different environmental/growth conditions (pH, temperature, carbon and nitrogen sources, antibiotics, etc.) is challenging. Thus, the pursuit of complementary approaches to quantify growth kinetics becomes mandatory in order to better understand fungal cell growth. Furthermore, it is well-known that filamentous fungi, including Aspergillus spp., have distinct modes of growth and differentiation under sub-aerial conditions on solid media or submerged cultures. Here, we detail a quantitative microscopic method for analyzing growth kinetics of&#160;the model fungus Aspergillus nidulans, using live imaging in both submerged cultures and solid media. We capture images, analyze, and quantify growth rates of different fungal strains in a reproducible and reliable manner using an open source, free software for bio-images (e.g., Fiji), in a way that does not require any prior image analysis expertise from the user.

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software

We present a label-free live imaging protocol using transmitted light microscopy techniques to capture images, analyze and quantify growth kinetics of the filamentous fungus A. nidulans in both submerged cultures and solid media. This protocol can be used in conjunction with fluorescence microscopy.

It is well established that colony growth of filamentous fungi, mostly dependent on changes in hyphae/mycelia apical growth rate, is macroscopically ...