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>
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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. 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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. 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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&amp;rsquo;s Modified Eagle&amp;rsquo;s Medium (DMEM)-Ham&amp;rsquo;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>NH&lt;sub&gt;4&lt;/sub&gt;Cl</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>NaN&lt;sub&gt;3&lt;/sub&gt;</td><td>Merck</td><td>1.06688.0100</td><td></td></tr><tr><td>200 &amp;micro;M syringe Filcons</td><td>BD Biosciences</td><td>340613</td><td></td></tr><tr><td>70 &amp;micro;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, &amp;kappa;/Negative Control Compensation Particles Set</td><td>BD Biosciences</td><td>552844</td><td></td></tr><tr><td>Anti-Mouse Ig, &amp;kappa;/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&amp;nbsp;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

17.4K 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 of Adipose Tissue Immune Cells

The discovery of increased macrophage infiltration in the adipose tissue (AT) of obese rodents and humans has led to an intensification of interest in immune cell contribution to local and systemic insulin resistance. Isolation and quantification of different immune cell populations in lean and obese AT is now a commonly utilized technique in immunometabolism laboratories; yet extreme care must be taken both in stromal vascular cell isolation and in the flow cytometry analysis so that the data obtained is reliable and interpretable. In this video we demonstrate how to mince, digest, and isolate the immune cell-enriched stromal vascular fraction. Subsequently, we show how to antibody label macrophages and T lymphocytes and how to properly gate on them in flow cytometry experiments. Representative flow cytometry plots from low fat-fed lean and high fat-fed obese mice are provided. A critical element of this analysis is the use of antibodies that do not fluoresce in channels where AT macrophages are naturally autofluorescent, as well as the use of proper compensation controls.

Adipose tissue (AT) is a site of intense immune cell activation and interaction. Almost all cells of the immune system are present in AT and their ratios are altered by obesity. Proper isolation, quantification, and characterization of AT immune cell populations are critical for understanding their role in immunometabolic disease.

The discovery of increased macrophage infiltration in  the adipose tissue (AT) of obese rodents and humans has led to an  intensification of interest in ...

Immunology and Infection

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

Granulocytes play a key role in the body&#8217;s innate immune response to bacterial and viral infections. While methods exist to measure granulocyte function, in general these are limited in terms of the information they can provide. For example, most existing assays merely provide a percentage of how many granulocytes are activated following a single, fixed length incubation. Complicating matters, most assays focus on only one aspect of function due to limitations in detection technology. This report demonstrates a technique for simultaneous measurement of granulocyte phagocytosis of bacteria and oxidative burst. By measuring both of these functions at the same time, three unique phenotypes of activated granulocytes were identified: 1) Low Activation (minimal phagocytosis, no oxidative burst), 2) Moderate Activation (moderate phagocytosis, some oxidative burst, but no co-localization of the two functional events), and 3) High Activation (high phagocytosis, high oxidative burst, co-localization of phagocytosis and oxidative burst). A fourth population that consisted of inactivated granulocytes was also identified. Using assay incubations of 10, 20, and 40-min the effect of assay incubation duration on the redistribution of activated granulocyte phenotypes was assessed. A fourth incubation was completed on ice as a control. By using serial time incubations, the assay may be able to able to detect how a treatment spatially affects granulocyte function. All samples were measured using an image-based flow cytometer equipped with a quantitative imaging (QI) option, autosampler, and multiple lasers (488, 642, and 785 nm).

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

This method demonstrates a technique for assessing granulocyte function by simultaneously measuring phagocytosis of bacteria and oxidative burst. Image-based flow cytometry allowed for the identification of three distinct subsets of activated granulocytes that all differed in their relative functional capacity.

Granulocytes play a key role in the body&#8217;s innate immune response to bacterial and viral infections. While methods exist to measure granulocyte ...

Characterization of Human Monocyte-derived Dendritic Cells by Imaging Flow Cytometry: A Comparison between Two Monocyte Isolation Protocols

Dendritic cells (DCs) are antigen presenting cells of the immune system that play a crucial role in lymphocyte responses, host defense mechanisms, and pathogenesis of inflammation. Isolation and study of DCs have been important in biological research because of their distinctive features. Although they are essential key mediators of the immune system, DCs are very rare in blood, accounting for approximately 0.1 - 1% of total blood mononuclear cells. Therefore, alternatives for isolation methods rely on the differentiation of DCs from monocytes isolated from peripheral blood mononuclear cells (PBMCs). The utilization of proper isolation techniques that combine simplicity, affordability, high purity, and high yield of cells is imperative to consider. In the current study, two distinct methods for the generation of DCs will be compared. Monocytes were selected by adherence or negatively enriched using magnetic separation procedure followed by differentiation into DCs with IL-4 and GM-CSF. Monocyte and MDDC viability, proliferation, and phenotype were assessed using viability dyes, MTT assay, and CD11c/ CD14 surface marker analysis by imaging flow cytometry. Although the magnetic separation method yielded a significant higher percentage of monocytes with higher proliferative capacity when compared to the adhesion method, the findings have demonstrated the ability of both techniques to simultaneously generate monocytes that are capable of proliferating and differentiating into viable CD11c+ MDDCs after seven days in culture. Both methods yielded &gt; 70% CD11c+ MDDCs. Therefore, our results provide insights that contribute to the development of reliable methods for isolation and characterization of human DCs.

This study compares two different methods of human monocyte isolation for obtaining in vitro dendritic cells (DCs). Monocytes are selected by adherence or negatively enriched by magnetic separation. Monocyte yield and viability along with MDDC viability, proliferation and CD11c/CD14 surface marker expression will be compared between both methods.

Dendritic cells (DCs) are antigen presenting cells of the immune system that play a crucial role in lymphocyte responses, host defense mechanisms, and ...

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Monocytes are key contributors in various inflammatory disorders and alterations to these cells, including their subset proportions and functions, can have pathological significance. An ideal method for examining alterations to monocytes is whole blood flow cytometry as the minimal handling of samples by this method limits artifactual cell activation. However, many different approaches are taken to gate the monocyte subsets leading to inconsistent identification of the subsets between studies. Here we demonstrate a method using whole blood flow cytometry to identify and characterize human monocyte subsets (classical, intermediate, and non-classical). We outline how to prepare the blood samples for flow cytometry, gate the subsets (ensure contaminating cells have been removed), and determine monocyte subset expression of surface markers &#8212; in this example M1 and M2 markers. This protocol can be extended to other studies that require a standard gating method for assessing monocyte subset proportions and monocyte subset expression of other functional markers.

Here we present a protocol for characterizing monocyte subsets by whole blood flow cytometry. This includes outlining how to gate the subsets and assess their expression of surface markers and giving an example of the assessment of the expression of M1 (inflammatory) and M2 markers (anti-inflammatory).

Monocytes are key contributors in various inflammatory disorders and alterations to these cells, including their subset proportions and functions, can ...

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. Considering an implant as a foreign body, a hostile and dysregulated immune response may result in the rejection of the implant, while a regulated response and regaining of homeostasis can lead to its acceptance. Analyzing the microenvironments of implants dissected out under in vivo or ex vivo&#160;settings can help in understanding the pattern of immune response, which can ultimately help in developing new generations of biomaterials. Flow cytometry is a well-known technique for characterizing immune cells and their subsets based on their cell surface markers. This review describes a protocol based on manual dicing, enzymatic digestion, and filtration through a cell strainer for the isolation of uniform cell suspensions from dissected implant tissue. Further, a multicolor flow cytometry staining protocol has been explained, along with steps for initial cytometer settings to characterize and quantify these isolated cells by flow cytometry.

Isolation of cells from dissected implants and their characterization by flow cytometry can significantly contribute to understanding the pattern of immune response against implants. This paper describes a precise method for the isolation of cells from dissected implants and their staining for flow cytometric analysis.

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. ...

Bioengineering

An Advanced Murine Model for Nonalcoholic Steatohepatitis in Association with Type 2 Diabetes

Obesity is associated with chronic low-grade inflammation and insulin resistance, contributing to an increasing prevalence of chronic metabolic diseases, such as type 2 diabetes and nonalcoholic steatohepatitis (NASH). Recent research has established that pro-inflammatory immune cells infiltrate obese hypertrophic adipose tissue and liver. Given the emerging importance of immune cells in the context of metabolic homeostasis, there is a critical need to quantify and characterize their modification during the development of type 2 diabetes and NASH. However, animal models that induce pathophysiological features typical of human NASH are sparse.
In this article, we provide a detailed protocol to identify immune cell subsets isolated from liver and adipose tissue in a reliable mouse model of NASH, established by housing high-fat diet (HFD) mice under non-specific pathogen-free (SPF) conditions without a barrier for at least seven weeks. We demonstrate the handling of mice in non-SPF conditions, digestion of the tissues and identification of macrophages, natural killer (NK) cells, dendritic cells, B and T cell subsets by flow cytometry. Representative flow cytometry plots from SPF HFD mice and non-SPF mice are provided. To obtain reliable and interpretable data, the use of antibodies, accurate and precise methods for tissue digestion and proper gating in flow cytometry experiments are critical elements.
The intervention to restore physiological antigen exposure in mice by housing them in non-SPF conditions and unspecific exposure to microbial antigens could provide a relevant tool for investigating the link between immunological alterations, diet-induced obesity and related long term complications.

A simple and reliable diet-induced rodent animal model for nonalcoholic steatohepatitis (NASH) is described, achieved through non-SPF housing of the animals and administration of a specific high-fat diet. We describe identification of hepatic and adipose immune cell subsets to recapitulate human immunological conditions by exposing mice to environmental germs.

Obesity is associated with chronic low-grade inflammation and insulin resistance, contributing to an increasing prevalence of chronic metabolic diseases, ...

Isolation of Adipogenic and Fibro-Inflammatory Stromal Cell Subpopulations from Murine Intra-Abdominal Adipose Depots

The stromal-vascular fraction (SVF) of white adipose tissue (WAT) is remarkably heterogeneous and consists of numerous cell types that contribute functionally to the expansion and remodeling of WAT in adulthood. A tremendous barrier to studying the implications of this cellular heterogeneity is the inability to readily isolate functionally distinct cell subpopulations from WAT SVF for in vitro and in vivo analyses. Single-cell sequencing technology has recently identified functionally distinct fibro-inflammatory and adipogenic PDGFR&#946;+ perivascular cell subpopulations in intra-abdominal WAT depots of adult mice. Fibro-inflammatory progenitors (termed, &#8220;FIPs&#8221;) are non-adipogenic collagen producing cells that can exert a pro-inflammatory phenotype. PDGFR&#946;+ adipocyte precursor cells (APCs) are highly adipogenic both in vitro and in vivo upon cell transplantation. Here, we describe multiple methods for the isolation of these stromal cell subpopulations from murine intra-abdominal WAT depots. FIPs and APCs can be isolated by fluorescence-activated cell sorting (FACS) or by taking advantage of biotinylated antibody-based immunomagnetic bead technology. Isolated cells can be used for molecular and functional analysis. Studying the functional properties of stromal cell subpopulation in isolation will expand our current knowledge of adipose tissue remodeling under physiological or pathological conditions on the cellular level.

This protocol describes the technical approach to isolate adipogenic and fibro-inflammatory stromal cell subpopulations from murine intra-abdominal white adipose tissue (WAT) depots by fluorescence-activated cell sorting or immunomagnetic bead separation.

The stromal-vascular fraction (SVF) of white adipose tissue (WAT) is remarkably heterogeneous and consists of numerous cell types that contribute ...

Flow Cytometric Analysis for Identification of the Innate and Adaptive Immune Cells of Murine Lung

The respiratory tract is in direct contact with the outside environment and requires a precisely regulated immune system to provide protection while suppressing unwanted reactions to environmental antigens. Lungs host several populations of innate and adaptive immune cells that provide immune surveillance but also mediate protective immune responses. These cells, which keep the healthy pulmonary immune system in balance, also participate in several pathological conditions such as asthma, infections, autoimmune diseases, and cancer. Selective expression of surface and intracellular proteins provides unique immunophenotypic properties to the immune cells of the lung. Consequently, flow cytometry has an instrumental role in the identification of such cell populations during steady-state and pathological conditions. This paper presents a protocol that describes a consistent and reproducible method to identify the immune cells that reside in the lungs of healthy mice under steady-state conditions. However, this protocol can also be used to identify changes in these cell populations in various disease models to help identify disease-specific changes in the lung immune landscape.

In this study, we present an effective and reproducible protocol to isolate the immune populations of the murine respiratory system. We also provide a method for the identification of all innate and adaptive immune cells that reside in the lungs of healthy mice, using a 9-color-based flow cytometry panel.

The respiratory tract is in direct contact with the outside environment and requires a precisely regulated immune system to provide protection while ...

Isolation of Viable Adipocytes and Stromal Vascular Fraction from Human Visceral Adipose Tissue Suitable for RNA Analysis and Macrophage Phenotyping

Visceral adipose tissue (VAT) is an active metabolic organ composed mainly of mature adipocytes and stromal vascular fraction (SVF) cells, which release different bioactive molecules that control metabolic, hormonal, and immune processes; currently, it is unclear how these processes are regulated within the adipose tissue. Therefore, the development of methods evaluating the contribution of each cell population to the pathophysiology of adipose tissue is crucial. This protocol describes the isolation steps and provides the necessary troubleshooting guidelines for efficient isolation of viable mature adipocytes and SVF from human VAT biopsies in a single process, using a collagenase enzymatic digestion technique. Moreover, the protocol is also optimized to identify macrophage subsets and perform mature adipocyte RNA isolation for gene expression studies, which allows performing studies dissecting the interaction between these cell populations. Briefly, VAT biopsies are washed, minced mechanically, and digested to generate a single-cell suspension. After centrifugation, mature adipocytes are isolated by flotation from the SVF pellet. The RNA extraction protocol ensures a high yield of total RNA (including miRNAs) from adipocytes for downstream expression assays. Simultaneously, SVF cells are used to characterize macrophage subsets (pro- and anti-inflammatory phenotype) through flow cytometry analysis.

This protocol provides an efficient collagenase digestion method for isolation of viable adipocytes and stromal vascular fraction-SVF cells from human visceral fat in a single process, including methodology to obtain high-quality RNA from adipocytes and phenotypification of SVF-macrophages through staining of multiple membrane-bound markers for analysis by flow cytometry.

Visceral adipose tissue (VAT) is an active metabolic organ composed mainly of mature adipocytes and stromal vascular fraction (SVF) cells, which release ...

Human Adipose Tissue Micro-fragmentation for Cell Phenotyping and Secretome Characterization

In the past decade, adipose tissue transplants have been widely used in plastic surgery and orthopaedics to enhance tissue repletion and/or regeneration. Accordingly, techniques for harvesting and processing human adipose tissue have evolved in order to quickly and efficiently obtain large amounts of tissue. Among these, the closed system technology represents an innovative and easy-to-use system to harvest, process, and re-inject refined fat tissue in a short time and in the same intervention (intra-operatively). Adipose tissue is collected by liposuction, washed, emulsified, rinsed and minced mechanically into cell clusters of 0.3 to 0.8 mm. Autologous transplantation of mechanically fragmented adipose tissue has shown remarkable efficacy in different therapeutic indications such as aesthetic medicine and surgery, orthopedic and general surgery. Characterization of micro-fragmented adipose tissue revealed the presence of intact small vessels within the adipocyte clusters; hence, the perivascular niche is left unperturbed. These clusters are enriched in perivascular cells (i.e., mesenchymal stem cell (MSC) ancestors) and in vitro analysis showed an increased release of growth factors and cytokines involved in tissue repair and regeneration, compared to enzymatically derived MSCs. This suggests that the superior therapeutic potential of microfragmented adipose tissue is explained by a higher frequency of presumptive MSCs and enhanced secretory activity. Whether these added pericytes directly contribute to higher growth factor and cytokine production is not known. This clinically approved procedure allows the transplantation of presumptive MSCs without the need for expansion and/or enzymatic treatment, thus bypassing the requirements of GMP guidelines, and reducing the costs for cell-based therapies.

Here, we present human adipose tissue enzyme-free micro-fragmentation using a closed system device. This new method allows the obtainment of sub-millimeter clusters of adipose tissue suitable for in vivo transplantation, in vitro culture, and further cell isolation and characterization.

In the past decade, adipose tissue transplants have been widely used in plastic surgery and orthopaedics to enhance tissue repletion and/or regeneration. ...

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

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Chapters in this video

Isolation of Adipose Tissue Immune Cells

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

Characterization of Human Monocyte-derived Dendritic Cells by Imaging Flow Cytometry: A Comparison between Two Monocyte Isolation Protocols

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

An Advanced Murine Model for Nonalcoholic Steatohepatitis in Association with Type 2 Diabetes

Isolation of Adipogenic and Fibro-Inflammatory Stromal Cell Subpopulations from Murine Intra-Abdominal Adipose Depots

Flow Cytometric Analysis for Identification of the Innate and Adaptive Immune Cells of Murine Lung

Isolation of Viable Adipocytes and Stromal Vascular Fraction from Human Visceral Adipose Tissue Suitable for RNA Analysis and Macrophage Phenotyping

Human Adipose Tissue Micro-fragmentation for Cell Phenotyping and Secretome Characterization