We would like to thank J. van de Gaar and M. Vroomen (Maastricht University, The Netherlands) for their technical support. In addition, we would like to thank K. Verboven, D. Hansen, J. Jocken, and E. Blaak for providing the blood and tissue biopsies used for setting up this protocol and the subsequent experiments.

Visceral and subcutaneous AT samples were taken from subjects enrolled in the study approved by the Medical Ethical committee Jessa Hospital, Hasselt, and Hasselt University, Belgium, in accordance with the Declaration of Helsinki.
1. Preparation of Reagents
<ol>
	<li>Collagenase solution
	<ol>
		<li>Dissolve 1 g of Collagenase I in 10 mL of phosphate buffered saline (PBS, without calcium and magnesium) to make a 100 mg/mL stock solution. Prepare 200 &#181;L aliquots and store at -20 &#176;C.</li>
		<li>Dissolve 1 g of Collagenase XI in 10 mL of PBS to make a 100 mg/mL stock solution. Prepare 200 &#181;L aliquots and store at -20 &#176;C.</li>
		<li>Dissolve 10 mg of DNase I in 10 mL of PBS to make a 10 mg/mL stock solution. Prepare 180 &#181;L aliquots and store at -20 &#176;C.</li>
		<li>Add 100 &#181;L Collagenase I (100 mg/mL), 100 &#181;L Collagenase XI (100 mg/mL), and 90 &#181;L DNase I (10 mg/mL) to 10 mL of DMEM Ham&#39;s F12. Make collagenase solution fresh for each isolation.</li>
	</ol>
	</li>
	<li>Erythrocyte lysis buffer
	<ol>
		<li>Dissolve 0.84 g NH4Cl in 100 mL of ultrapure water.</li>
		<li>Set the pH at 7.4 before use. Store in a glass flask at 4 &#176;C.</li>
		<li>Place the erythrocyte lysis buffer on ice before use.</li>
	</ol>
	</li>
	<li>FACS buffer
	<ol>
		<li>Dissolve 0.5 g bovine serum albumin (BSA) in 100 mL of PBS to obtain 0.5% BSA PBS.</li>
		<li>Dissolve 65 mg of NaN3 in 100 mL 0.5% BSA PBS to obtain 10 mM NaN3 0.5% BSA PBS. Store solution in a glass flask at 4 &#176;C.</li>
		<li>Place FACS buffer on ice before use. 
		CAUTION: NaN3 is highly toxic. Work in a fume hood and wear safety glasses and gloves for protection while handling NaN3.</li>
	</ol>
	</li>
	<li>Human IgG block
	<ol>
		<li>Dissolve 10 mg of human IgG in 10 mL PBS to obtain 1 mg/mL. Prepare 100 &#181;L aliquots and store at -20 &#176;C.</li>
		<li>Place the human IgG block on ice before use.</li>
	</ol>
	</li>
</ol>
2. Isolation of SVF from AT
<ol>
	<li>Cut 1 g of AT biopsy into small pieces (&#177; 2 mm2) with a scalpel and transfer to a 50-mL centrifuge tube (e.g., Falcon tube). Add 10 mL of collagenase solution to each AT sample. 
	NOTE: Close the lid of the tube completely and turn the lid &#188; turn back.</li>
	<li>Incubate for 60 min at 37 &#176;C in a water-bath under gentle shaking (60 cycles/min).</li>
	<li>Filter the resulting suspension with a 200 &#181;M filter and collect the sample in a new 50-mL centrifuge tube. Add 7 mL PBS on top of the filter to rinse the filter and obtain all the cells.</li>
	<li>Centrifuge the sample at 280 x g for 5 min at 4 &#176;C.</li>
	<li>Remove the floating adipocyte fraction by pipetting. The cell pellet is the SVF. 
	NOTE: Remove the adipocyte fraction to obtain SVF. Avoid submerging the entire tip in the sample because this will only remove the PBS and not the floating adipocytes.</li>
	<li>Resuspend the SVF in 5 mL of PBS to remove collagenase, filter the suspension with a 70 &#181;M filter, rinse the filter with 5 mL PBS, and centrifuge the sample at 280 x g for 5 min at 4 &#176;C.</li>
	<li>Remove the supernatant and resuspend the pellet in 3 mL of erythrocyte lysis buffer.</li>
	<li>Incubate for 5 min on ice. Add 7 mL of PBS after incubation.</li>
	<li>Centrifuge the sample at 280 x g for 5 min at 4 &#176;C.</li>
</ol>
3. Staining of SVF for Flow Cytometry Analysis
<ol>
	<li>Dissolve the cell pellet in 90 &#181;L 4 &#176;C FACS buffer and add 10 &#181;L of 1 mg/mL human IgG block. Divide the cell suspension in 2 wells of a 96 v-shape well plate. Place the plate on ice and let the human IgG block incubate for 15 min.</li>
	<li>Add 100 &#181;L FACS buffer to each sample to wash and centrifuge the plate for 5 min with 280 x g at 4 &#176;C. Remove the supernatant by tipping the plate upside down in one smooth movement without tapping the plate. 
	NOTE: Make sure to remove any remaining liquid from the top of the plate with a tissue while keeping the plate upside down.</li>
	<li>Prepare antibody cocktails for macrophage and DC subsets (FACS panel 1) and for T- and B-cell subsets (FACS panel 2) as described in Table 1 and Table 2. The volumes described in Table 1 and Table 2 are selected after optimizing the antibody concentrations and are sufficient for one vAT or sAT sample. 
	NOTE: In FACS panel 1, use the markers CD303 and CD141 to confirm that CD11C+ CD11Blow cells are DCs. However, it is recommended to exclude these markers from the panel to include a live/dead staining. Both FACS panel 1 and 2 can be combined with the LIVE/DEAD Fixable Red Dead Cell Stain Kit viability staining when excluding CD303 in panel 1 as the PE channel will be unused. Perform viability staining according to the manufacturer&#39;s instructions.</li>
	<li>Resuspend the pellet in 29.5 &#181;L antibody cocktail for FACS panel 1, and 23 &#181;L antibody cocktail for FACS panel 2. Incubate for 30 min in the dark on ice.</li>
	<li>Add 150 &#181;L FACS buffer to each well and resuspend the cell pellet to perform a second wash step. Centrifuge the plate for 5 min at 280 x g and 4 &#176;C. Remove the supernatant by tipping the plate upside down.</li>
	<li>Add 150 &#181;L 1% formaldehyde solution to each well to fix the cells. Transfer the cell suspension from each well to the corresponding FACS tube by pipetting with a P200 pipette. Store FACS tubes at 4 &#176;C in the dark up to 7 days. 
	NOTE: Direct measurement is also possible. Add 150 &#181;L FACS buffer to each well instead of 1% formaldehyde, transfer the cells by pipetting with a P200 pipette to the corresponding FACS tubes, and analyze the cells. 
	CAUTION: Formaldehyde is very toxic. Prepare formaldehyde solutions while working in a fume hood to avoid inhalation and wear gloves and safety glasses for protection.</li>
</ol>
4. Flow Cytometry Analysis
<ol>
	<li>Before the first measurement, use an unstained negative control to set the forward scatter (FSC) and side scatter (SSC). Adjust the voltages of the flow cytometer according to the manufacturer&#39;s instructions so that all populations of interest are visible in the FSC and SSC graph and a distinction between debris and live cells can be made.</li>
	<li>Perform multi-color compensation analysis with antibody capture beads following the manufacturer&#39;s protocol.</li>
	<li>Prepare fluorescence minus one (FMO) controls by making the antibody mix but exclude one antibody from the mix. Do this for every antibody, creating 8 antibody mixes for FACS panel 1, and 6 antibody mixes for FACS panel 2. These FMO antibody mixes are used to stain SVF as described previously in this protocol.</li>
	<li>Measure all FMO controls and set the gating strategy based on FMO controls. Use the FMO controls to detect possible auto-fluorescence of the cells. 
	NOTE: By removing one antibody from the mix, any fluorescence level detected in this channel is a background/autofluorescent signal. Hence, by comparing the different FMO control FACS results, gates can be drawn on specific populations ensuring that the gatings are based on positive cells and not based on auto-fluorescence.</li>
	<li>Vortex the FACS tubes at 800 rpm before placing them in the flow cytometer and starting the measurement. 
	NOTE: A minimum of 50,000 events in the live gate is recommended to ensure enough cells are measured from each subpopulation.</li>
</ol>

<ol>
	<li>McNelis, J. C., Olefsky, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macrophages,+immunity,+and+metabolic+disease.">Macrophages, immunity, and metabolic disease.</a> Immunity. 41 (1), 36-48 (2014).</li><li>Makki, K., Froguel, P., Wolowczuk, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adipose+tissue+in+obesity-related+inflammation+and+insulin+resistance:+cells,+cytokines,+and+chemokines.">Adipose tissue in obesity-related inflammation and insulin resistance: cells, cytokines, and chemokines.</a> ISRN Inflamm. 2013, 139239 (2013).</li><li>DeFuria, 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=B+cells+promote+inflammation+in+obesity+and+type+2+diabetes+through+regulation+of+T-cell+function+and+an+inflammatory+cytokine+profile.">B cells promote inflammation in obesity and type 2 diabetes through regulation of T-cell function and an inflammatory cytokine profile.</a> Proc Natl Acad Sci U S A. 110 (13), 5133-5138 (2013).</li><li>Elgazar-Carmon, V., Rudich, A., Hadad, N., Levy, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neutrophils+transiently+infiltrate+intra-abdominal+fat+early+in+the+course+of+high-fat+feeding.">Neutrophils transiently infiltrate intra-abdominal fat early in the course of high-fat feeding.</a> J Lipid Res. 49 (9), 1894-1903 (2008).</li><li>Han, J. M., Levings, 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=Immune+regulation+in+obesity-associated+adipose+inflammation.">Immune regulation in obesity-associated adipose inflammation.</a> J Immunol. 191 (2), 527-532 (2013).</li><li>Liu, 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=Genetic+deficiency+and+pharmacological+stabilization+of+mast+cells+reduce+diet-induced+obesity+and+diabetes+in+mice.">Genetic deficiency and pharmacological stabilization of mast cells reduce diet-induced obesity and diabetes in mice.</a> Nat Med. 15 (8), 940-945 (2009).</li><li>Nishimura, 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=CD8++effector+T+cells+contribute+to+macrophage+recruitment+and+adipose+tissue+inflammation+in+obesity.">CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity.</a> Nat Med. 15 (8), 914-920 (2009).</li><li>Koh, Y. 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=Stromal+vascular+fraction+from+adipose+tissue+forms+profound+vascular+network+through+the+dynamic+reassembly+of+blood+endothelial+cells.">Stromal vascular fraction from adipose tissue forms profound vascular network through the dynamic reassembly of blood endothelial cells.</a> Arterioscler Thromb Vasc Biol. 31 (5), 1141-1150 (2011).</li><li>Peinado, J. 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A., Reynolds, C. M., McGillicuddy, F. C., Roche, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fats,+inflammation+and+insulin+resistance:+insights+to+the+role+of+macrophage+and+T-cell+accumulation+in+adipose+tissue.">Fats, inflammation and insulin resistance: insights to the role of macrophage and T-cell accumulation in adipose tissue.</a> Proc Nutr Soc. 70 (4), 408-417 (2011).</li><li>Lumeng, C. N., Bodzin, J. L., Saltiel, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Obesity+induces+a+phenotypic+switch+in+adipose+tissue+macrophage+polarization.">Obesity induces a phenotypic switch in adipose tissue macrophage polarization.</a> J Clin Invest. 117 (1), 175-184 (2007).</li><li>Morris, D. L., Singer, K., Lumeng, C. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adipose+tissue+macrophages:+phenotypic+plasticity+and+diversity+in+lean+and+obese+states.">Adipose tissue macrophages: phenotypic plasticity and diversity in lean and obese states.</a> Curr Opin Clin Nutr Metab Care. 14 (4), 341-346 (2011).</li><li>Wentworth, J. 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=Pro-inflammatory+CD11c+CD206++adipose+tissue+macrophages+are+associated+with+insulin+resistance+in+human+obesity.">Pro-inflammatory CD11c+CD206+ adipose tissue macrophages are associated with insulin resistance in human obesity.</a> Diabetes. 59 (7), 1648-1656 (2010).</li><li>Wouters, K., 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=Circulating+classical+monocytes+are+associated+with+CD11c++macrophages+in+human+visceral+adipose+tissue.">Circulating classical monocytes are associated with CD11c+ macrophages in human visceral adipose tissue.</a> Sci Rep. 7, 42665 (2017).</li><li>Duan, 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=Distinct+macrophage+subpopulations+characterize+acute+infection+and+chronic+inflammatory+lung+disease.">Distinct macrophage subpopulations characterize acute infection and chronic inflammatory lung disease.</a> J Immunol. 189 (2), 946-955 (2012).</li><li>Li, F., 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=Autofluorescence+contributes+to+false-positive+intracellular+Foxp3+staining+in+macrophages:+a+lesson+learned+from+flow+cytometry.">Autofluorescence contributes to false-positive intracellular Foxp3 staining in macrophages: a lesson learned from flow cytometry.</a> J Immunol Methods. 386 (1-2), 101-107 (2012).</li></ol>

The authors declare no conflicts of interest.

These methods describe how to isolate the SVF from vAT and sAT and quantify the relative amounts of immune cells within these tissues. Furthermore, the methods state how to determine the expression of markers on specific cell types.
Flow cytometry of tissue immune cells is a powerful technique to phenotype the immunological state of tissues. The quantification of tissue immune cells can have many applications. As described in the results, it is possible to compare the presence of specific immune cells between groups of patients (e.g., lean vs. obese). In addition, by also performing flow cytometry on blood of the same patients, associations between circulating cells and tissue cells can be investigated. With this application we were able to determine that a specific subset of circulating monocytes is associated with pro-inflammatory CD11C+ adipose tissue macrophages16.
Adjustments to the described protocol will expand its applications as numerous available fluorescent antibodies make flow cytometry very versatile. With different antibodies nearly all cell types can be distinguished and the expression of many markers can be detected. Furthermore, it is possible to stain markers intracellularly by permeabilizing the cell membrane to allow intracellular binding of the fluorescent antibodies. These characteristics allow distinction of the very diverse macrophage populations beyond the overly simplified M1 and M2 macrophage subtypes. Besides measurement of surface marker expression, proteins (i.e., cytokines) can be stained intracellularly providing information on macrophage functionality. In addition, proliferation markers such as Ki67 are used to quantify proliferation rates. As described, distinction between macrophages and DCs was based on MFI levels of DC markers. A general macrophage marker, such as CD68 can be incorporated into the macrophage panel (FACS panel 1). However, CD68 needs to be stained intracellularly requiring permeabilization of the cell membrane which is not preferable and would extend the protocol. Other macrophage markers are subset markers such as CD163 and CD206 or CD11c, the latter being integrated in the macrophage panel presented here.
In our FACS panels, a marker to distinguish live and dead cells was not included, which would be preferable because it allows a more accurate exclusion of dead cells than the use of FSC and SSC. Frequently used are the DNA staining viability dyes propidium iodide (PI) or 4&#39;,6-diamidino-2-phenylindole (DAPI) as well as free amine reacting dyes such as the LIVE/DEAD Fixable Dead Cell Stain Kit, which is available in different dye colors. However, PI and DAPI cannot be used when fixing the cells. As described in the protocol, the LIVE/DEAD Fixable Red Dead Cell viability staining can be integrated into both panels without affecting the overall FACS gating strategy.
In addition, the data are expressed as a percentage of live cells meaning all data are relative. Only by entering an exact, known number of cells into the flow cytometer, would it be possible to determine the exact numbers of each cell type. An approximate number of cells could be calculated after counting the cells in the SVF fraction by using a counting chamber. However, this number would have to be adjusted for the amount of biopsy tissue used to isolate the SVF, but this has limitations when comparing lean to obese AT. A similar mass of obese AT consists of less adipocytes as they are filled with lipids and have expanded greatly. This could lead to an underestimation of immune cell number if presented as number of immune cells per gram of AT or per adipocyte.
In human studies, inclusion of patients is usually done over a longer period of time making standardization of experimental procedures of great importance. For comparison of flow cytometry data between patients, there are several options. As described in this protocol, cells can be fixed before measurement allowing analysis of several samples on the same day. This can also be achieved by freezing the SVF before staining them, which allows the staining procedure to be equal between all samples, but the viability of cells might be affected. Lastly, also employed in this study, are fluorescent beads to install compensation levels, and cytometer tracking beads were used bi-weekly to standardize daily measurements of the cytometer. This last option is the most efficient when measuring samples from a study spanning a long period of time.
A limiting factor for flow cytometry in general is the use of fluorescence. The number of fluorescent labels that can be detected simultaneously is limited due to overlap in emission spectra. However, with smart FACS panel development and the use of several antibody cocktails per vAT or sAT sample, this issue can be overcome as described in this protocol. An important aspect of FACS panel development is FMO controls. By using all antibodies of the panel except for one, potential autofluorescence levels can be appreciated when comparing the FMO with the full panel. This allows accurate gating of populations and these procedures should be performed when setting up a new FACS panel. In addition, new generations of FACS devices can detect up to 50 parameters allowing simultaneous detection of many characteristics per cell. Another issue related to the fluorescence aspect is the autofluorescence of cells, particularly macrophages. After excitation of the cells with the FACS laser (mainly with 488 nm wavelength excitation), these cells emit a fluorescent signal (mainly &#60;640 nm) that can overlap with the emission spectra of the antibody labels17,18. To account for this, unstained cells should be measured to determine the autofluorescence in each channel. With this knowledge, fluorochromes should be selected that display a signal strength that exceeds the autofluorescent signal. This autofluorescent background signal should be taken into account when determining the gating strategy of the populations. Therefore, by application of this protocol and intelligent FACS panel design it is possible to in depth phenotype macrophage subtypes. New distinct AT macrophages and their function can be characterized.

Obesity is characterized with low-grade AT inflammation1 and infiltration of pro-inflammatory immune cells in both visceral and subcutaneous AT (vAT, sAT). Accumulation of pro-inflammatory immune cells in the vAT leads to insulin resistance which is a primary risk factor for developing type 2 diabetes2. Immune cells of both the innate and adaptive immune system are found in the obese AT, such as macrophages, mast cells, neutrophils, CD4+ and CD8+ T-cells, and B-cells3,4,5,6,7. These immune cells, together with endothelial cells, stromal cells, adipocyte progenitors, fibroblasts, and pericytes, constitute the SVF8 and are the main source of pro-inflammatory substances in the AT9.
The inflammatory status of AT is commonly investigated by techniques including Western blot10, qPCR11, and immunohistochemistry11. However, when using these techniques, the entire AT, adipocytes, and SVF, is used. This makes it difficult to determine the amount and subsets of immune cells present in the AT. Immune cells have various cell markers to define and categorize them, such as macrophages. Macrophages show significant heterogeneity in both function and cell surface marker expression12. Therefore, they are often categorized into two macrophage populations: M1 and M2. M2 macrophages are usually called alternatively activated macrophages12,13 and reside in the AT of lean, metabolically normal humans14. However, during obesity, a phenotypic switch occurs from M2 macrophages to M1 macrophages. These classically activated M1 macrophages express CD11C12 and accumulate around dead adipocytes to form crown-like structures13. It has been shown that CD11C+ macrophages in the AT impair insulin action and are associated with insulin resistance in obese humans15. To identify M1 and M2 macrophages in the AT, immunohistochemistry is an option. This technique gives information about the location of the macrophages in the tissue. However, it will limit the number of markers that can be used in one staining. Moreover, it is also difficult to quantify. Therefore, to investigate the different immune cell subsets in the vAT and sAT deposits, we have developed a flow cytometry approach. This approach gives us the opportunity to use multiple markers per cell with one flow cytometry analysis to define cell subsets and count the numbers of each subset present in the AT deposits.

<table>
	<tbody>
		<tr>
			<td>Centrifuge</td>
			<td>Hettich Rotanta 460R</td>
			<td>5660</td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#8217;s Modified Eagle&#8217;s Medium (DMEM)-Ham&#8217;s F12</td>
			<td>Gibco ThermoFisher</td>
			<td>31330-095</td>
			<td></td>
		</tr>
		<tr>
			<td>Collagenase XI from Clostridium histolyticum</td>
			<td>Sigma Aldrich</td>
			<td>C7657-1g</td>
			<td></td>
		</tr>
		<tr>
			<td>Collagenase I from Clostridium histolyticum</td>
			<td>Sigma Aldrich</td>
			<td>C0130-1g</td>
			<td></td>
		</tr>
		<tr>
			<td>DNAse I from bovine pancreas</td>
			<td>Sigma Aldrich</td>
			<td>DN25</td>
			<td></td>
		</tr>
		<tr>
			<td>NH4Cl</td>
			<td>Merck</td>
			<td>1.01145.0500</td>
			<td></td>
		</tr>
		<tr>
			<td>Bovine Serum Albumine</td>
			<td>Sigma Aldrich</td>
			<td>A4503-100</td>
			<td></td>
		</tr>
		<tr>
			<td>NaN3</td>
			<td>Merck</td>
			<td>1.06688.0100</td>
			<td></td>
		</tr>
		<tr>
			<td>200 &#181;M syringe Filcons</td>
			<td>BD Biosciences</td>
			<td>340613</td>
			<td></td>
		</tr>
		<tr>
			<td>70 &#181;M filter</td>
			<td>Greiner Bio-one</td>
			<td>542070</td>
			<td></td>
		</tr>
		<tr>
			<td>Fc block / human IgG</td>
			<td>Sigma Aldrich</td>
			<td>14506</td>
			<td></td>
		</tr>
		<tr>
			<td>Formaldehyde</td>
			<td>Merck</td>
			<td>1.04003.2500</td>
			<td></td>
		</tr>
		<tr>
			<td>CD11b-BV421</td>
			<td>Biolegend</td>
			<td>301324</td>
			<td></td>
		</tr>
		<tr>
			<td>CD19-Fitc</td>
			<td>BD Biosciences</td>
			<td>555412</td>
			<td></td>
		</tr>
		<tr>
			<td>CD3-Fitc</td>
			<td>BD Biosciences</td>
			<td>561807</td>
			<td></td>
		</tr>
		<tr>
			<td>CD66b-Fitc</td>
			<td>BD Biosciences</td>
			<td>555724</td>
			<td></td>
		</tr>
		<tr>
			<td>CD56-Fitc</td>
			<td>BD Biosciences</td>
			<td>562794</td>
			<td></td>
		</tr>
		<tr>
			<td>CD11c-APC-Cy7</td>
			<td>Biolegend</td>
			<td>337218</td>
			<td></td>
		</tr>
		<tr>
			<td>CD45-PE-Cy7</td>
			<td>BD Biosciences</td>
			<td>557748</td>
			<td></td>
		</tr>
		<tr>
			<td>CD19-BV421</td>
			<td>Biolegend</td>
			<td>302234</td>
			<td></td>
		</tr>
		<tr>
			<td>CD3-V500</td>
			<td>BD Biosciences</td>
			<td>561416</td>
			<td></td>
		</tr>
		<tr>
			<td>CD56-APC</td>
			<td>Biolegend</td>
			<td>318310</td>
			<td></td>
		</tr>
		<tr>
			<td>CD4-PerCP-Cy5.5</td>
			<td>Biolegend</td>
			<td>300528</td>
			<td></td>
		</tr>
		<tr>
			<td>CD8-APC-H7</td>
			<td>BD Biosciences</td>
			<td>641400</td>
			<td></td>
		</tr>
		<tr>
			<td>CD303-PE</td>
			<td>Biolegend</td>
			<td>354204</td>
			<td></td>
		</tr>
		<tr>
			<td>CD141-APC</td>
			<td>Biolegend</td>
			<td>344106</td>
			<td></td>
		</tr>
		<tr>
			<td>FACS-Canto II</td>
			<td>BD Biosciences</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>96 v-shape well plate</td>
			<td>Greiner Bio-one</td>
			<td>651101</td>
			<td></td>
		</tr>
		<tr>
			<td>PBS PH 7.4</td>
			<td>Gibco ThermoFisher</td>
			<td>10010023</td>
			<td></td>
		</tr>
		<tr>
			<td>FACS tube 5 mL polystyrene round-bottom tube</td>
			<td>Corning</td>
			<td>352052</td>
			<td></td>
		</tr>
		<tr>
			<td>IgG from human serum</td>
			<td>Sigma Aldrich</td>
			<td>I4506</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-Rat Ig, &#954;/Negative Control Compensation Particles Set</td>
			<td>BD Biosciences</td>
			<td>552844</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-Mouse Ig, &#954;/Negative Control Compensation Particles Set</td>
			<td>BD Biosciences</td>
			<td>552843</td>
			<td></td>
		</tr>
		<tr>
			<td>LIVE/DEAD Fixable Red Dead Cell Stain Kit</td>
			<td>ThermoFisher</td>
			<td>L23102</td>
			<td></td>
		</tr>
		<tr>
			<td>IKA&#160;MS 3 basic shaker</td>
			<td>Sigma Aldrich</td>
			<td>Z645028-1EA</td>
			<td></td>
		</tr>
	</tbody>
</table>

characterization of immune cells in human adipose tissue by using flow cytometry

Infiltration of immune cells in the subcutaneous and visceral adipose tissue (AT) deposits leads to a low-grade inflammation contributing to the development of obesity-associated complications such as type 2 diabetes. To quantitatively and qualitatively investigate the immune cell subsets in human AT deposits, we have developed a flow cytometry approach. The stromal vascular fraction (SVF), containing the immune cells, is isolated from subcutaneous and visceral AT biopsies by collagenase digestion. Adipocytes are removed after centrifugation. The SVF cells are stained for multiple membrane-bound markers selected to differentiate between immune cell subsets and analyzed using flow cytometry. As a result of this approach, pro- and anti-inflammatory macrophage subsets, dendritic cells (DCs), B-cells, CD4+ and CD8+ T-cells, and NK cells can be detected and quantified. This method gives detailed information about immune cells in AT and the amount of each specific subset. Since there are numerous fluorescent antibodies available, our flow cytometry approach can be adjusted to measure various other cellular and intracellular markers of interest.

The SVF isolated from vAT and sAT was measured using flow cytometry. Flow cytometry measurements generate plots showing different cell populations based on cellular markers (Figure 1A and 1B). First, by plotting the forward scatter width (FSC-W) and forward scatter area (FSC-A), cell aggregates can be eliminated from further analysis by gating the single cells as low FSC-W. Next, live cells are selected, and cellular debris is excluded by gating the cells of the correct size and complexity using FSC-A and the side scatter area (SSC-A), respectively. Dead cells are small and therefore visible as a distinct population with a small FSC-A. Next, immune cells were selected by use of the pan-leukocyte marker CD45 (Panel 1 and 2, Figure 1A). To analyze macrophages, other immune cells such as T-cells (CD3), B-cells (CD19), neutrophils (CD66b+ CD11b+), and NK-cells (CD56) were excluded from further analysis by using distinct antibodies targeting these cells, but with the same fluorochrome. Further subdivision of the remaining cells was based on CD11b and CD11c expression. This resulted in the following populations: CD11b+ CD11c+ macrophages, CD11b+ CD11c- macrophages, and CD11blow/- CD11c+ DCs (FACS panel 1, Figure 1B). Measurement of mean fluorescence intensity (MFI) allowed the quantification of the expression of CD303 (plasmacytoid DC marker) and CD141 (DC marker), on CD11b+ CD11c+ macrophages and CD11blow/- CD11c+ DCs. Expression of both these markers were higher in CD11blow/- CD11c+ cells confirming that CD11blow/- CD11c+ cells were DCs (Figure 1C).
The CD45+ cells (Figure 1A) were divided into T-cells and B-cells using CD3 and CD19, respectively. T-cells were subdivided into T-helper cells (CD4+) and cytotoxic T-cells (CD8+). Lastly, CD3-CD19-cells were plotted to quantify NK-cells using the marker CD56 (FACS panel 2, Figure 1D). The number of cells in each gate is quantified and can be used to calculate the percentage of this cell type of all living cells (Table 3).
The percentage of living cells can be calculated for each subject allowing the calculation of an average of all subjects in a group of for example lean or obese men displaying the abundance of a specific immune cell, i.e., the pro-inflammatory CD11b+ CD11c+ macrophage in visceral AT (Figure 2).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57319/57319fig1.jpg" /> 
Figure 1. FACS gating strategy of visceral adipose tissue. (A) FACS plot of all events (black) with forward scatter width intensity (FSC-W) and forward scatter area intensity (FSC-A) containing a gate to select only single cells (red) followed by a FACS plot based on FSC-A and side scatter area intensity (SSC-A) containing a gate selecting live cells (light green). Next plot with SSC-A and CD45 fluorescence intensity contains a gate selecting all CD45+ (immune) cells (blue). (B) FACS plot of CD19, CD3, CD66b, and CD56 fluorescence intensity versus CD11b fluorescence intensity, and a gate selecting all cells that are CD19, CD3, CD66b, and CD56 negative (brown) for further division into populations. Further subdivision in the next plot based on CD11b and CD11c fluorescence intensity. Gates are displayed containing CD11b+ CD11c+ macrophages (dark green), CD11b+ CD11c- macrophages (purple), and CD11blow/- CD11c+ dendritic cells (blue). (C) Amount of CD11b+, CD11c+, or CD11blow/- CD11c+ cells (Y-axis) displaying their levels of fluorescence intensity (X-axis) for CD303 and CD141, and the corresponding quantification of the mean fluorescence intensity (MFI). (D) FACS plot displaying the previous CD45+ population (blue) based on CD3 and CD19 fluorescence intensity containing gates selecting T-cells (magenta), B-cells (dark green), and non-autofluorescent cells (green) negative for both CD3 and CD19. The following plot is based on CD4 and CD8 fluorescence with gates selecting CD4+ (light green) and CD8+ (magenta) T-cells. An identical gating strategy is used for subcutaneous adipose tissue. An identical gating strategy is used for subcutaneous adipose tissue. This figure has been modified from Wouters et al.16 <a href="https://cloudflare.jove.com/files/ftp_upload/57319/57319fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57319/57319fig2.jpg" /> 
Figure 2. Obese vAT contains more pro-inflammatory macrophages. The amount of CD11b+ CD11c+ macrophages presented as percentage of all living cells in vAT of lean and obese men. All data are means &#177; SEM; n = 20 for lean and n = 31 for obese. **p&#160;&#8804; 0.01 vs. lean. <a href="https://cloudflare.jove.com/files/ftp_upload/57319/57319fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Target</td>
			<td>Definition target</td>
			<td>Present on</td>
			<td>Fluorochrome</td>
			<td>Quantity&#160;</td>
			<td>Clone</td>
		</tr>
		<tr>
			<td>CD11B</td>
			<td>myeloid integrin marker</td>
			<td>granulocytes, monocytes/macrophages, dendritic cellslow and natural killer cells</td>
			<td>BV421</td>
			<td>2.5 &#181;L</td>
			<td>ICRF44</td>
		</tr>
		<tr>
			<td>CD19</td>
			<td>common B cell antigen</td>
			<td>B cell development from pro-B cell to blastoid B cell and plasma B cell</td>
			<td>Fitc</td>
			<td>3 &#181;L</td>
			<td>HIB19</td>
		</tr>
		<tr>
			<td>CD3</td>
			<td>common T cell antigen</td>
			<td>T lymphocytes, natural killer T cells and thymocytes&#160;</td>
			<td>Fitc</td>
			<td>3 &#181;L</td>
			<td>UCHT1</td>
		</tr>
		<tr>
			<td>CD66B</td>
			<td>member of the carcinoembryonic antigen (CEA)-like glycoprotein family</td>
			<td>granulocytes</td>
			<td>Fitc</td>
			<td>5 &#181;L</td>
			<td>G10F5</td>
		</tr>
		<tr>
			<td>CD56</td>
			<td>heavily glycosylated adhesion protein</td>
			<td>natural killer cells and natural killer T cells</td>
			<td>Fitc</td>
			<td>5 &#181;L</td>
			<td>B159</td>
		</tr>
		<tr>
			<td>CD303</td>
			<td>type II transmembrane glycoprotein</td>
			<td>plasmacytoid dendritic cells</td>
			<td>PE</td>
			<td>1 &#181;L</td>
			<td>201A</td>
		</tr>
		<tr>
			<td>CD141</td>
			<td>thrombomodulin</td>
			<td>monocytes/macrophageslow, subpopulation of dendritic cells</td>
			<td>APC&#160;</td>
			<td>1 &#181;L</td>
			<td>M80</td>
		</tr>
		<tr>
			<td>CD11C</td>
			<td>type I transmembrane glycoprotein; integrin &#945;x</td>
			<td>monocytes/macrophages, dendritic cells, granulocytes, natural killer cells, subset of B and T cells</td>
			<td>APC-Cy7</td>
			<td>0.5 &#181;L</td>
			<td>Bu15</td>
		</tr>
		<tr>
			<td>CD45</td>
			<td>common leukocyte antigen</td>
			<td>all human leukocytes including lymphocytes, monocytes, granulocytes, eosinophils, and thymocytes</td>
			<td>PE-Cy7</td>
			<td>1 &#181;L</td>
			<td>HI30</td>
		</tr>
		<tr>
			<td>FACS buffer</td>
			<td>-</td>
			<td>-</td>
			<td>-</td>
			<td>7.5 &#181;l</td>
			<td>-</td>
		</tr>
	</tbody>
</table>
Table 1. Antibody cocktail for FACS panel 1 to identify macrophage subsets and dendritic cells populations. Amount of antibody described is for the analysis of one sample.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Target</td>
			<td>Definition target</td>
			<td>Present on</td>
			<td>Fluorochrome</td>
			<td>Quantity</td>
			<td>Clone</td>
		</tr>
		<tr>
			<td>CD19</td>
			<td>common B cell antigen</td>
			<td>B cell development from pro-B cell to blastoid B cell and plasma B cell</td>
			<td>BV421</td>
			<td>1 &#181;L</td>
			<td>HIB19</td>
		</tr>
		<tr>
			<td>CD3</td>
			<td>common T cell antigen</td>
			<td>T lymphocytes, natural killer T cells and thymocytes</td>
			<td>V500</td>
			<td>3 &#181;L</td>
			<td>UCHT1</td>
		</tr>
		<tr>
			<td>CD56</td>
			<td>heavily glycosylated adhesion protein</td>
			<td>natural killer cells and natural killer T cells</td>
			<td>APC</td>
			<td>5 &#181;L</td>
			<td>HCD56</td>
		</tr>
		<tr>
			<td>CD4</td>
			<td>Ig superfamily, type I transmembrane glycoprotein</td>
			<td>T helper cell, thymocytes, monocytes/macrophages, type II natural killer T cells</td>
			<td>PerCP-Cy5.5</td>
			<td>1 &#181;L</td>
			<td>RPA-T4</td>
		</tr>
		<tr>
			<td>CD8</td>
			<td>&#945;-subunit of a disulfide-linked bimolecular complex</td>
			<td>cytotoxic T cells, thymocytes, subset of natural killer cells</td>
			<td>APC-H7</td>
			<td>2 &#181;L</td>
			<td>SK1</td>
		</tr>
		<tr>
			<td>CD45</td>
			<td>common leukocyte antigen</td>
			<td>all human leukocytes including lymphocytes, monocytes, granulocytes, eosinophils, and thymocytes</td>
			<td>PE-Cy7</td>
			<td>1 &#181;L</td>
			<td>HI30</td>
		</tr>
		<tr>
			<td>FACS buffer</td>
			<td>-</td>
			<td>-</td>
			<td>-</td>
			<td>10 &#181;l</td>
			<td>-</td>
		</tr>
	</tbody>
</table>
Table 2. Antibody cocktail for FACS panel 2to identify T- and B-cell populations. Amount of antibody described is for the analysis of one sample.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Macrophage panel</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Gate name</td>
			<td># Cells</td>
			<td>% of live</td>
		</tr>
		<tr>
			<td>Single cells</td>
			<td>183054</td>
			<td></td>
		</tr>
		<tr>
			<td>live</td>
			<td>10477</td>
			<td>100</td>
		</tr>
		<tr>
			<td>CD45</td>
			<td>4100</td>
			<td>39.13</td>
		</tr>
		<tr>
			<td>dump-</td>
			<td>771</td>
			<td>7.36</td>
		</tr>
		<tr>
			<td>CD11C- CD11B+</td>
			<td>430</td>
			<td>4.1</td>
		</tr>
		<tr>
			<td>CD11C+ CD11B+</td>
			<td>104</td>
			<td>0.99</td>
		</tr>
		<tr>
			<td>CD11C+ CD11Blow/-</td>
			<td>15</td>
			<td>0.14</td>
		</tr>
		<tr>
			<td>T-cell, B-cell, NK-cell panel</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Gate name</td>
			<td>#Cells</td>
			<td>% of live</td>
		</tr>
		<tr>
			<td>Single cells</td>
			<td>34616</td>
			<td></td>
		</tr>
		<tr>
			<td>Live</td>
			<td>3728</td>
			<td>100</td>
		</tr>
		<tr>
			<td>CD45+</td>
			<td>1589</td>
			<td>42.62</td>
		</tr>
		<tr>
			<td>NK-cells</td>
			<td>29</td>
			<td>0.78</td>
		</tr>
		<tr>
			<td>CD3+</td>
			<td>953</td>
			<td>25.56</td>
		</tr>
		<tr>
			<td>CD4+</td>
			<td>601</td>
			<td>16.12</td>
		</tr>
		<tr>
			<td>CD8+</td>
			<td>328</td>
			<td>8.8</td>
		</tr>
		<tr>
			<td>CD19+</td>
			<td>20</td>
			<td>0.54</td>
		</tr>
	</tbody>
</table>
Table 3. Immune cell abundance of different cell types in vAT. Number of cells in each gate and the percentage of the different cell types based on the total amount of living cells.

Watch this Scientific Journal Video about Characterization of Immune Cells in Human Adipose Tissue by Using Flow Cytometry at JoVE.com

Characterization of Immune Cells in Human Adipose Tissue by Using Flow Cytometry

This article describes a method to analyze immune cell content of adipose tissue by isolation of immune cells from adipose tissue and subsequent analysis using flow cytometry.

Infiltration of immune cells in the subcutaneous and visceral adipose tissue (AT) deposits leads to a low-grade inflammation contributing to the ...

characterization-immune-cells-human-adipose-tissue-using-flow

Universidad de Cartagena UDEC

Research

JoVE Journal

Medicine

17.3K Views.  MUMC. This article describes a method to analyze immune cell content of adipose tissue by isolation of immune cells from adipose tissue and subsequent analysis using flow cytometry.

Video: Characterization of Immune Cells in Human Adipose Tissue by Using Flow Cytometry

Isolation, Characterization and Comparative Differentiation of Human Dental Pulp Stem Cells Derived from Permanent Teeth by Using Two Different Methods

Developing wisdom teeth are easy-accessible source of stem cells during the adulthood which could be obtained by routine orthodontic treatments. Human pulp-derived stem cells (hDPSCs) possess high proliferation potential with multi-lineage differentiation capacity compare to the ordinary source of adult stem cells1-8; therefore, hDPSCs could be the good candidates for autologous transplantation in tissue engineering and regenerative medicine. Along with these benefits, possessing the mesenchymal stem cells (MSC) features, such as immunolodulatory effect, make hDPSCs more valuable, even in the case of allograft transplantation6,9,10. Therefore, the primary step for using this source of stem cells is to select the best protocol for isolating hDPSCs from pulp tissue. In order to achieve this goal, it is crucial to investigate the effect of various isolation conditions on different cellular behaviors, such as their common surface markers &amp; also their differentiation capacity.
Thus, here we separate human pulp tissue from impacted third molar teeth, and then used both existing protocols based on literature, for isolating hDPSCs,11-13 i.e. enzymatic dissociation of pulp tissue (DPSC-ED) or outgrowth from tissue explants (DPSC-OG). In this regards, we tried to facilitate the isolation methods by using dental diamond disk. Then, these cells characterized in terms of stromal-associated Markers (CD73, CD90, CD105 &amp; CD44), hematopoietic/endothelial Markers (CD34, CD45 &amp; CD11b), perivascular marker, like CD146 and also STRO-1. Afterwards, these two protocols were compared based on the differentiation potency into odontoblasts by both quantitative polymerase chain reaction (QPCR) &amp; Alizarin Red Staining. QPCR were used for the assessment of the expression of the mineralization-related genes (alkaline phosphatase; ALP, matrix extracellular phosphoglycoprotein; MEPE &amp; dentin sialophosphoprotein; DSPP).14

The method described isolation and characterization of human Dental Pulp Stem Cells (hDPSCs) by using either enzymatic dissociation of pulp (DPSC-ED) or direct outgrowth of stem cells from pulp tissue explants (DPSC-OG). Then followed by in vitro comparative differentiation of both types of hDPSCs into odontoblasts.

Developing wisdom teeth are easy-accessible source of stem cells during the adulthood which could be obtained by routine orthodontic treatments. Human ...

High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum

Over the recent years, antibodies against surface and conformational proteins involved in neurotransmission have been detected in autoimmune CNS diseases in children and adults. These antibodies have been used to guide diagnosis and treatment. Cell-based assays have improved the detection of antibodies in patient serum. They are based on the surface expression of brain antigens on eukaryotic cells, which are then incubated with diluted patient sera followed by fluorochrome-conjugated secondary antibodies. After washing, secondary antibody binding is then analyzed by flow cytometry. Our group has developed a high-throughput flow cytometry live cell-based assay to reliably detect antibodies against specific neurotransmitter receptors. This flow cytometry method is straight forward, quantitative, efficient, and the use of a high-throughput sampler system allows for large patient cohorts to be easily assayed in a short space of time. Additionally, this cell-based assay can be easily adapted to detect antibodies to many different antigenic targets, both from the central nervous system and periphery. Discovering additional novel antibody biomarkers will enable prompt and accurate diagnosis and improve treatment of immune-mediated disorders.

Over the recent years, live cell-based assays have been used successfully to detect antibodies against surface and conformational antigens. Here, we describe a method using high-throughput flow cytometry enabling the analysis of large cohorts of patients. Detection of novel antibodies will improve diagnosis and treatment of immune-mediated disorders.

Over the recent years, antibodies against surface and conformational proteins involved in neurotransmission have been detected in autoimmune CNS diseases ...

Stereological and Flow Cytometry Characterization of Leukocyte Subpopulations in Models of Transient or Permanent Cerebral Ischemia

Microglia activation, as well as extravasation of haematogenous macrophages and neutrophils, is believed to play a pivotal role in brain injury after stroke. These myeloid cell subpopulations can display different phenotypes and functions and need to be distinguished and characterized to study their regulation and contribution to tissue damage. This protocol provides two different methodologies for brain immune cell characterization: a precise stereological approach and a flow cytometric analysis. The stereological approach is based on the optical fractionator method, which calculates the total number of cells in an area of interest (infarcted brain) estimated by a systematic random sampling. The second characterization approach provides a simple way to isolate brain leukocyte suspensions and to characterize them by flow cytometry, allowing for the characterization of microglia, infiltrated monocytes and neutrophils of the ischemic tissue. In addition, it also details a cerebral ischemia model in mice that exclusively affects brain cortex, generating highly reproducible infarcts with a low rate of mortality, and the procedure for histological brain processing to characterize infarct volume by the Cavalieri method.

Brain myeloid cells characterization following stroke can be performed by stereology using the optical fractionator method, or by a flow cytometric analysis of brain leukocytes suspensions. Both are useful techniques to perform an accurate phenotypical distinction of the main myeloid cell subsets found in the ischemic brain.

Microglia activation, as well as extravasation of haematogenous macrophages and neutrophils, is believed to play a pivotal role in brain injury after ...

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Reliably differentiating brown adipose tissue (BAT) from other tissues using a non-invasive imaging method is an important step toward studying BAT in humans. Detecting BAT is typically confirmed by the uptake of the injected radioactive tracer 18F-Fluorodeoxyglucose (18F-FDG) into adipose tissue depots, as measured by positron emission tomography/computed tomography (PET-CT) scans after exposing the subject to cold stimulus. Fat-water separated magnetic resonance imaging (MRI) has the ability to distinguish BAT without the use of a radioactive tracer. To date, MRI of BAT in adult humans has not been co-registered with cold-activated PET-CT. Therefore, this protocol uses 18F-FDG PET-CT scans to automatically generate a BAT mask, which is then applied to co-registered MRI scans of the same subject. This approach enables measurement of quantitative MRI properties of BAT without manual segmentation. BAT masks are created from two PET-CT scans: after exposure for 2 hr to either thermoneutral (TN) (24 &#176;C) or cold-activated (CA) (17 &#176;C) conditions. The TN and CA PET-CT scans are registered, and the PET standardized uptake and CT Hounsfield values are used to create a mask containing only BAT. CA and TN MRI scans are also acquired on the same subject and registered to the PET-CT scans in order to establish quantitative MRI properties within the automatically defined BAT mask. An advantage of this approach is that the segmentation is completely automated and is based on widely accepted methods for identification of activated BAT (PET-CT). The quantitative MRI properties of BAT established using this protocol can serve as the basis for an MRI-only BAT examination that avoids the radiation associated with PET-CT.

The method presented here uses 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET-CT) and fat-water separated magnetic resonance imaging (MRI), each scanned following 2 hr exposure to thermoneutral (24 &#176;C) and cold conditions (17 &#176;C) in order to map brown adipose tissue (BAT) in adult human subjects.

Reliably differentiating brown adipose tissue (BAT) from other tissues using a non-invasive imaging method is an important step toward studying BAT in ...

Studying Pancreatic Cancer Stem Cell Characteristics for Developing New Treatment Strategies

Pancreatic ductal adenocarcinoma (PDAC) contains a subset of exclusively tumorigenic cancer stem cells (CSCs) which have been shown to drive tumor initiation, metastasis and resistance to radio- and chemotherapy. Here we describe a specific methodology for culturing primary human pancreatic CSCs as tumor spheres in anchorage-independent conditions. Cells are grown in serum-free, non-adherent conditions in order to enrich for CSCs while their more differentiated progenies do not survive and proliferate during the initial phase following seeding of single cells. This assay can be used to estimate the percentage of CSCs present in a population of tumor cells. Both size (which can range from 35 to 250 micrometers) and number of tumor spheres formed represents CSC activity harbored in either bulk populations of cultured cancer cells or freshly harvested and digested tumors 1,2. Using this assay, we recently found that metformin selectively ablates pancreatic CSCs; a finding that was subsequently further corroborated by demonstrating diminished expression of pluripotency-associated genes/surface markers and reduced in vivo tumorigenicity of metformin-treated cells. As the final step for preclinical development we treated mice bearing established tumors with metformin and found significantly prolonged survival. Clinical studies testing the use of metformin in patients with PDAC are currently underway (e.g., NCT01210911, NCT01167738, and NCT01488552). Mechanistically, we found that metformin induces a fatal energy crisis in CSCs by enhancing reactive oxygen species (ROS) production and reducing mitochondrial transmembrane potential. In contrast, non-CSCs were not eliminated by metformin treatment, but rather underwent reversible cell cycle arrest. Therefore, our study serves as a successful example for the potential of in vitro sphere formation as a screening tool to identify compounds that potentially target CSCs, but this technique will require further in vitro and in vivo validation to eliminate false discoveries.

Pancreatic cancer stem cells (CSCs) can be expanded in vitro using the anchorage-independent sphere culture technique, which represents a powerful tool to study CSC biology and can serve as the first step to develop novel CSC-targeting therapies. Here the methodology for expanding, analyzing and targeting of pancreatic CSCs is provided.

Pancreatic ductal adenocarcinoma (PDAC) contains a subset of exclusively tumorigenic cancer stem cells (CSCs) which have been shown to drive tumor ...

Encapsulation Thermogenic Preadipocytes for Transplantation into Adipose Tissue Depots

Cell encapsulation was developed to entrap viable cells within semi-permeable membranes. The engrafted encapsulated cells can exchange low molecular weight metabolites in tissues of the treated host to achieve long-term survival. The semipermeable membrane allows engrafted encapsulated cells to avoid rejection by the immune system. The encapsulation procedure was designed to enable a controlled release of bioactive compounds, such as insulin, other hormones, and cytokines. Here we describe a method for encapsulation of catabolic cells, which consume lipids for heat production and energy dissipation (thermogenesis) in the intra-abdominal adipose tissue of obese mice. Encapsulation of thermogenic catabolic cells may be potentially applicable to the prevention and treatment of obesity and type 2 diabetes. Another potential application of catabolic cells may include detoxification from alcohols or other toxic metabolites and environmental pollutants.

Here, we present a protocol for encapsulation of catabolic cells, which consume lipids for heat production in intra-abdominal adipose tissue and increase energy dissipation in obese mice.

Cell encapsulation was developed to entrap viable cells within semi-permeable membranes. The engrafted encapsulated cells can exchange low molecular ...

Assessment of Human Adipose Tissue Microvascular Function Using Videomicroscopy

While obesity is closely linked to the development of metabolic and cardiovascular disease, little is known about mechanisms that govern these processes. It is hypothesized that pro-atherogenic mediators released from fat tissues particularly in association with central/visceral adiposity may promote pathogenic vascular changes locally and systemically, and the notion that cardiovascular disease may be the consequence of adipose tissue dysfunction continues to evolve. Here, we describe a unique method of videomicroscopy that involves analysis of vasodilator and vasoconstrictor responses of intact small human arterioles removed from the adipose depot of living human subjects. Videomicroscopy is used to examine functional properties of isolated microvessels in response to pharmacological or physiological stimuli using a pressured system that mimics in vivo conditions. The technique is a useful approach to gain understanding of the pathophysiology and molecular mechanisms that contribute to vascular dysfunction locally within the adipose tissue milieu. Moreover, abnormalities in the adipose tissue microvasculature have also been linked with systemic diseases. We applied this technique to examine depot-specific vascular responses in obese subjects. We assessed endothelium-dependent vasodilation to both increased flow and acetylcholine in adipose arterioles (50 - 350 &#181;m internal diameter, 2 - 3 mm in length) isolated from two different adipose depots during bariatric surgery from the same individual. We demonstrated that arterioles from visceral fat exhibit impaired endothelium-dependent vasodilation compared to vessels isolated from the subcutaneous depot. The findings suggest that the visceral microenvironment is associated with vascular endothelial dysfunction which may be relevant to clinical observation linking increased visceral adiposity to systemic disease mechanisms. The videomicroscopy technique can be used to examine vascular phenotypes from different fat depots as well as compare findings across individuals with different degrees of obesity and metabolic dysfunction. The method can also be used to examine vascular responses longitudinally in response to clinical interventions.

Videomicroscopy systems are used to examine functional properties of isolated adipose tissue arterioles in response to physiological and pharmacological stimuli. This technique can be used to examine microvascular phenotypes in different adipose tissue domains in obese humans.

While obesity is closely linked to the development of metabolic and cardiovascular disease, little is known about mechanisms that govern these processes. ...

Whole Body and Regional Quantification of Active Human Brown Adipose Tissue Using 18F-FDG PET/CT

In endothermic animals, brown adipose tissue (BAT) is activated to produce heat for defending body temperature in response to cold. BAT&#39;s ability to expend energy has made it a potential target for novel therapies to ameliorate obesity and associated metabolic disorders in humans. Though this tissue has been well studied in small animals, BAT&#39;s thermogenic capacity in humans remains largely unknown due to the difficulties of measuring its volume, activity, and distribution. Identifying and quantifying active human BAT is commonly performed using 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography and computed tomography (PET/CT) scans following cold-exposure or pharmacological activation. Here we describe a detailed image-analysis approach to quantify total-body human BAT from 18F-FDG PET/CT scans using an open-source software. We demonstrate the drawing of user-specified regions of interest to identify metabolically active adipose tissue while avoiding common non-BAT tissues, to measure BAT volume and activity, and to further characterize its anatomical distribution. Although this rigorous approach is time-consuming, we believe it will ultimately provide a foundation to develop future automated BAT quantification algorithms.

Whole Body and Regional Quantification of Active Human Brown Adipose Tissue Using 18F-FDG PET/CT

Using free, open-source software, we have developed an analytical approach to quantify total and regional brown adipose tissue (BAT) volume and metabolic activity of BAT using 18F-FDG PET/CT.

In endothermic animals, brown adipose tissue (BAT) is activated to produce heat for defending body temperature in response to cold. BAT&#39;s ability to ...

Flow Cytometry Analysis of Immune Cell Subsets within the Murine Spleen, Bone Marrow, Lymph Nodes and Synovial Tissue in an Osteoarthritis Model

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases, affecting patients suffering from pain and physical limitations. Recent evidence indicates a potential inflammatory component of the disease, with both T-cells and monocytes/macrophages potentially associated with the pathogenesis of OA. Further studies postulated an important role for subsets of both inflammatory cell lineages, such as Th1, Th2, Th17, and T-regulatory lymphocytes, and M1, M2, and synovium-tissue-resident macrophages. However, the interaction between the local synovial and systemic inflammatory cellular response and the structural changes in the joint is unknown. To fully understand how T-cells and monocytes/macrophages contribute towards OA, it is important to be able to quantitively identify these cells and their subsets simultaneously in synovial tissue, secondary lymphatic organs and systemically (the spleen and bone marrow). Nowadays, the different inflammatory cell subsets can be identified by a combination of cell-surface markers making multi-color flow cytometry a powerful technique in investigating these cellular processes. In this protocol, we describe detailed steps regarding the harvest of synovial tissue and secondary lymphatic organs as well as generation of single cell suspensions. Furthermore, we present both an extracellular staining assay to identify monocytes/macrophages and their subsets as well as an extra- and intra-cellular staining assay to identify T-cells and their subsets within the murine spleen, bone marrow, lymph nodes and synovial tissue. Each step of this protocol was optimized and tested, resulting in a highly reproducible assay that can be utilized for other surgical and non-surgical OA mouse models.

Here, we describe a detailed and reproducible flow cytometry protocol to identify monocyte/macrophage and T-cell subsets using both extra- and intracellular staining assays within the murine spleen, bone marrow, lymph nodes and synovial tissue, utilizing an established surgical model of murine osteoarthritis.

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases, affecting patients suffering from pain and physical limitations. Recent ...

Fat-Covered Islet Transplantation Using Epididymal White Adipose Tissue

Islet transplantation is a cellular replacement therapy for severe diabetes mellitus. The intraperitoneal cavity is typically the transplant site for this procedure. However, intraperitoneal islet transplantation has some limitations, including poor transplant efficacy, difficult graft detection ability, and a lack of graftectomy capability for post-transplant analysis. In this paper, "fat-covered islet transplantation", an intraperitoneal islet transplantation method that utilizes epididymal white adipose tissue, is used to assess the therapeutic effects of bioengineered islets. The simplicity of the method lies in the seeding of islets onto epididymal white adipose tissue and using the tissue to cover the islets. While this method can be categorized as an intraperitoneal islet transplantation technique, it shares characteristics with intra-adipose tissue islet transplantation. The fat-covered islet transplantation method demonstrates more robust therapeutic effects than intra-adipose tissue islet transplantation, however, including the improvement of blood glucose and plasma insulin levels and the potential for graft removal. We recommend the adoption of this method for assessing the mechanisms of islet engraftment into white adipose tissue and the therapeutic effects of bioengineered islets.

This fat-covered islet transplantation method is suitable for the detection of engrafted islets in the intraperitoneal cavity. Notably, it does not require the use of biobinding agents or suturing.

Islet transplantation is a cellular replacement therapy for severe diabetes mellitus. The intraperitoneal cavity is typically the transplant site for this ...