Name: characterization of human monocyte subsets by whole blood flow cytometry analysis
Uploaded: 2018-10-17T12:30:00.000+00:00
Duration: 9 min 12 s
Description: Watch this Scientific Journal Video about Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis at JoVE.com

Flow cytometry was performed in the Flow Cytometry Core Facility that is supported by the Westmead Institute for Medical Research, Westmead Research Hub, Cancer Institute New South Wales, and National Health and Medical Research Council. This study was supported by Clinical Chemistry Research and Education Fund.

This study has been approved by the WSLHD Human Research Ethics committee (HREC) (approval AU RED HREC/15/WMEAD/289).
1. Sample Preparation for Whole Blood Flow Cytometry
NOTE: As human blood is potentially infectious, the sample set-up should be performed in a biohazard hood.
<ol>
	<li>Collect the blood samples from the participants into 3 mL ethylene diamine tetra acetic acid (EDTA) tubes.</li>
	<li>Determine the white blood cell (WBC) count using a hematology analyzer or hemocytometer.</li>
	<li>Dilute with phosphate buffered saline (PBS) (pH ~7.4) to adjust the concentration to ~5 x 106 WBC/mL.</li>
	<li>Prepare sufficient master mix for the number of tubes (e.g., for 14 tubes, prepare 16x master mix) by combining 16x volumes of 50 &#181;L blood, 0.75 &#181;L anti CD14-V450, 0.5 &#181;L anti CD16-APC, and 0.625 &#181;L anti HLA-DR-PerCP. Vortex and pipette 51.9 &#181;L of mix into each tube (Table 1). 
	NOTE: Antibodies should be titrated to determine optimal staining concentrations for the fluorescent antibodies used.</li>
	<li>Add surface marker (M1 and M2 or isotype control, phycoerythrin (PE) labelled) antibodies (example as per Table 2) and PE labelled markers for T cells (CD3), B cells (CD19), neutrophils (CD66b), and natural killer (NK) cells (CD56) (Table 3). Vortex and incubate for 30 min, 4 &#176;C in the dark. 
	NOTE: Markers for lymphocytes, neutrophils, and NK cells are included only for the validation of the gating method.</li>
	<li>Add 250 &#181;L of combined red blood cell lysis/WBC fixing solution, vortex gently immediately, and incubate for 10 min in the dark at 4 &#176;C.</li>
	<li>Add 250 &#181;L of PBS and spin cells down at 260 x g for 10 min at room temperature.</li>
	<li>Remove supernatant, re-suspend cells in 300 &#181;L of 1% formaldehyde. 
	NOTE: Formaldehyde is toxic. Use nitrile gloves and use in fume hood.</li>
	<li>Store at 4 &#176;C protected from light until analysis is performed. 
	NOTE: Flow analysis is recommended to be done within 48 h of sample preparation.</li>
</ol>
2. Flow Cytometry
<ol>
	<li>Check flow cytometer log to ensure facility staff have performed quality control checks. 
	NOTE: To ensure consistency between analyses, instrument quality control and maintaining consistent target fluorescence intensities using control beads are recommended.</li>
	<li>To set up the flow cytometry experiment, click on &#8220;New experiment then &#8220;new specimen&#8221; and &#8220;new tube&#8221; to add tubes. Select bivariate plots by clicking on the icon and use the dropdown menus to select the axis parameters. Ensure inclusion of a CD16/CD14 plot and a plot displaying a detector alongside time to monitor the acquisition.</li>
	<li>Insert tube and click &#8220;Acquire&#8221;. Check the instrument voltage settings ensuring that detector signals are not off scale.</li>
	<li>Observe cells falling in the monocyte gate of the CD14/CD16 plot. Set recording threshold to 5,000 events for the classical monocyte gate and click on &#8220;Record&#8221;.</li>
	<li>Continue to record data for remaining tubes. After data for all tubes has been recorded, export flow data as .fcs&#160;files for analysis. 
	NOTE: To ensure accuracy, single color compensation controls should be recorded. A compensation matrix can be calculated and applied to the data before analysis to account for spectral spill over15,16.</li>
</ol>
3. Monocyte Gating
<ol>
	<li>Open files in the analysis software. Double click tube name and select parameters from the dropdown menus to visualize the cells on a forward scatter area FSC(A)/forward scatter height FSC(H) plot. Create a doublet exclusion gate by clicking on the polygon gate tool icon and enclosing the cells as in (Figure 1A).</li>
	<li>Select the gated cells (by double clicking on the gated region) and in the new display box adjust dropdown menu parameters to display the cells on an FSC(A)/side scatter SSC(A) plot. Click on the rectangular gate icon and generously select the monocyte population based on forward and side scatter properties to exclude the majority of lymphocytes, NK cells, and granulocytes (Figure 1B).</li>
	<li>Select the gated cells and redisplay on a CD14/CD16 plot, selecting the parameters by using the dropdown menus. Click on the polygon gate to select monocytes based on their characteristic &#8220;&#9488;&#8221; shape (Figure 1C).</li>
	<li>Select the gated cells and display the monocytes on a CD16/HLA-DR plot by using the dropdown menus to select parameters. Click on the polygon gate to select the HLA-DR positive cells and exclude any remaining NK cells and neutrophils17 (Figure 1D).</li>
	<li>Select the gated cells and display the HLA-DR positive cells on a CD14/HLA-DR plot using dropdown menus to select parameters. Click on the polygon gate and draw a gate to exclude the HLA-DR high/CD14 low cells (B cells express high levels of HLA-DR but not CD14) (Figure 1E). 
	NOTE: B cell contamination may occur and therefore should be investigated. If the non-classical population in Figure 1C is not distinct from the cells to its left, then contamination is likely. Step 3.5 can be skipped if B cells are not overlapping with non-classical monocytes.</li>
	<li>Select the gated cells and use dropdown menus to display them on a CD16/CD14 plot. From plot options select &#8220;Zebra plot&#8221; which will enable monocyte subset gates to be drawn to determine subset proportions (Figure 1F). 
	NOTE: If zebra plot is unavailable on the analysis software, pseudo color (smooth) or contour plot may be suitable.</li>
	<li>Click on the rectangular gate icon and select the classical monocytes by drawing an approximate rectangular gate around the CD14 high/CD16 low, classical monocyte population. Under &#8220;Display&#8221; select &#8220;Show medians&#8221; to display the median fluorescence intensity for classical monocytes. Adjust the gate such that the population has an equal distribution from the median on the left and right encompassing all the cells to the left.</li>
	<li>Select the intermediate population by drawing a rectangular gate that encompasses the cells to the right of the classical gate. Adjust the bottom of the gate to exclude the non-classical cells by aligning the gate with the bottom of the concentric circles that are completely within the classical monocyte gate, which ensures that the intermediate subset has a CD14 expression comparable to the main classical population consistent with the current nomenclature.</li>
	<li>Gate the non-classical subset by drawing a rectangular box down from the lower edge of the intermediate subset, selecting all the cells to the bottom of the population (Figure 1F).</li>
	<li>Under &#8220;Display&#8221; select &#8220;Show gate frequencies&#8221; to determine the percentage of each monocyte subset.</li>
</ol>
4. Validation of Gating Method
<ol>
	<li>To determine whether potentially contaminating cell types are effectively gated-out, first identify different cell populations with antibodies against T cells (CD3), B cells (CD19), neutrophils (CD66b), and NK cells (CD56) (Figure 2). 
	NOTE: Here T cells and neutrophils do not sit close to the &#8220;&#9488;&#8221; shape of monocytes and are gated out in Figure 1C.</li>
	<li>Confirm that NK cells are removed in step 3.4 (Figure 1D), and B cells are removed in step 3.5 (Figure 1E) as shown in Figure 3. If NK cells or B cells are not gated-out, re-adjust the gates.</li>
</ol>
5. Phenotypic Monocyte Marker Expression
<ol>
	<li>Select cells from each monocyte subset. Alter dropdown parameters to create a histogram for each monocyte subset (Figure 1F) displaying each marker and its matching isotype (Figure 4).</li>
	<li>Calculate the degree of expression of each marker (median or geometric mean) compared to the respective isotype control.</li>
</ol>

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The authors have nothing to disclose.

Whole blood flow cytometry is an ideal approach to study monocytes as the cells are examined in conditions close to their physiological microenvironment providing an insight into their roles in infection and inflammatory conditions. Furthermore, the use of fresh (i.e., unprocessed) blood samples minimizes the alterations or cell transformations that can occur due to storage or handling18,19, such as those known to occur with freeze-thawed monocytes20. Prompt sample preparation is recommended as some markers are upregulated if samples are kept at room temperature prior to processing19. The optimal concentrations of M1 and M2 markers were determined by titration, and this should be done for any new antibody to limit non-specific binding whilst ensuring that the degree of shift is due to antigen expression and not restricted by lack of antibody. The removal of red blood cells and fixation of white blood cells with lysis solution is an important step in this protocol as the presence of red blood cells can interfere with flow cytometry21,22. Note that while some lysis solutions are compatible with no-wash staining, clearer populations are evident in our hands when a wash step is used.
Correct setup of the flow cytometer is also critical when comparing expression of monocyte markers. We recommend that researchers maintain consistent target fluorescence intensities of control beads and perform quality control on the instrument to be used to provide consistent results across different samples run on different days. In addition to this, isotype controls are used to assist in interpreting any non-specific background signal generated by non-specific antibody binding. Monocytes have high levels of Fc receptors11 and therefore are prone to non-specific binding. Of note, the level of non-specific binding differs for the different subsets, and thus the use of an isotype control becomes important when comparing the degree of marker expression between subsets.
Another important criterion to be considered is the gating steps employed. Some studies suggest that it is crucial to draw a tight gate around the monocyte population in FSC(A)/SSC(A) plot to get rid of most of the non-monocytic CD16 positive cells23,24,25, but this may lead to loss of some monocytes as non-monocyte cells can overlap with monocytes on FSC/SSC plots26. Rather, to exclude any other blood cells that may contaminate the monocytes, the inclusion of a third monocyte marker in addition to CD14 and CD16, is essential26,27. For this reason, HLA-DR is often used and is ideal as it is not expressed by NK cells or neutrophils17,28. Although lymphocytes (B cells and T cells) may express HLA-DR, they differ from monocytes in respect to CD14 expression. While HLA-DR is an ideal third marker, CD86 has also been recommended5,27,29 but was not used here as it is also an M1 marker and thus its degree of expression on monocyte subsets was assessed.
Validation of the gating strategy used is of crucial importance. While NK cells are known to overlap with non-classicals if they are not gated out28, we notice that B cells can also overlap with the non-classicals (Figure 2B); whether this is the case in other studies may depend on the fluorochrome choice, instrument configuration, detector sensitivity, or even the disease condition being examined. Here, the overlapping B cells showed high expression of HLA-DR and were not gated out by selection of HLA-DR positive cells in Figure 1D. Rather, to remove B cells we used an additional plot of CD14/ HLA-DR, where the B cells separate out from non-classicals due to their higher HLA-DR and low CD14 expression.
There are also many different ways in which the gates for the monocytes themselves have been drawn in the literature; these include quadrants (the subsets are separated by quadrant markers), and rectangular or trapezoid boxes13 (with separate boxes drawn for each subset) which furthermore differ in their placement delineating where one subset ends and another starts. These differences likely reflect the fact that monocytes exist as a continuum of cells, differentiating from classical to non-classical, rather than as clearly distinct populations. However, because variations in techniques for identifying the subsets themselves can lead to differences in the calculated monocyte subset proportions, it becomes important that the gating method is reasonably objective, rather than subjective, as this will make the method more robust and reproducible. Some studies use an isotype control for CD16 to determine the border between the classical and intermediate subsets30. On the other hand, to define the separation between intermediates and non-classicals, it has been proposed that the cut-off line may be vertical or oblique, with the choice up to the investigators, the proviso being it should be reproducible, but a rectangular gate has been recommended to facilitate comparisons between studies13,30,31. Here, increased objectiveness was obtained by plotting the data on a zebra plot and applying objective visual rules, because zebra plots provide an additional visualization cue by mixing color gradient to each equal probability bin over a traditional contour plot. The right border of the classical subset was drawn such that the population was evenly distributed around the population median. The division between the intermediates and non-classicals were also standardized by having the base of the intermediates align with the bottom of the concentric circles within the classical population (i.e., the intermediate population clearly expresses high levels of CD14, as per the standard nomenclature1).
While some studies have suggested the use of additional markers, such as C-C chemokine receptor type 2 (CCR2) or 6-sulfo LacNAc (SLAN) for obtaining successful enumeration of monocytes and to reveal their clinical significance32,33, in our hands the level of expression of many monocyte functional markers varies widely between individuals14. Such variation may limit the usefulness of these markers to define subsets based on their expression. Automated computational approaches have also been used to visualize and cluster monocyte subsets including visual interactive stochastic neighbor embedding (viSNE), t-distributed stochastic neighbor embedding (tSNE), or Spanning-tree Progression Analysis of Density-normalized Events (SPADE)34,35, which can provide visual representation of the cells based on the set of multiple markers used. While this has been shown to increase the accuracy of the gating strategy in monocyte subset classification, it is recognized that a drawback is the number of antibodies (and corresponding fluorophore channels) required. Its usefulness will depend on the questions being asked; the extra complexity may not be warranted, for example, in enumeration studies.
Monocytes gated with our technique show proportions in line with the literature and the expression of surface markers by the three subsets can be readily determined. Overall, the technique and methodology explained here provides a standardized and straightforward method of enumerating monocyte subset proportions and assessing surface marker expression, which can be extended to include other markers as well, thereby validating their functional roles in various other conditions.

Monocytes are a type of white blood cells which play a major role in promoting and resolving inflammation. There are three main subsets of monocytes recognized, classical (~85%), intermediate (~5%), and non-classical (~10%) monocytes, which are characterized by their level of cluster of differentiation (CD)14 and CD16 expression1. The proportions of monocyte subsets can differ with the presence of disease, such as an increased proportion of intermediates in various inflammatory states2,3 including cardiovascular disease, where the level of intermediates is associated with clinical events4,5. Furthermore, in disease conditions, monocytes can also undergo functional changes, with many changes detectable by a difference in surface marker expression6,7. One such example is monocyte M1-skewing, an increase in markers associated with M1 macrophages, which has been observed in cardiovascular disease, diabetes, obesity, and metabolic syndrome7,8,9,10.
Despite the popularity of flow cytometry to assess monocyte subset proportion and function, there is a considerable variability in sample preparation and subset gating between studies which makes it difficult to compare findings between such studies. Importantly, there is no consensus in the demarcation of monocyte subsets, yet a standardized approach is essential given the clinical significance of changes in subset proportions in several diseases. Part of the difficulty in gating arises from the fact that monocytes differentiate from the classical through the intermediate to the non-classical subset11 and as such, monocytes exist as a continuous spectrum rather than distinct populations12. Interestingly, Zawada et al. showed that using either a rectangular or trapezoid gating of the intermediate subset, both resulted in a higher intermediate subset that predicted a cardiovascular endpoint13. This highlights that, at least for calculating proportions, the key issue is applying a consistent gating strategy between different samples (and studies), rather than attempting to definitively discriminate between subsets. While definitive gating may be more important when assessing function, the change in marker expression between subsets is incremental12,14, and thus again, consistency in gating is perhaps key. As such, an objective gating method that reproducibly apportions the monocyte subsets between different samples is needed. The purpose of the method presented here is to gate monocyte subsets with a clear explanation and justification for the gating technique employed and assess the subsets for surface marker expression, thus providing a method which allows researchers to have confidence in the use of this technique when assessing different samples.

<table><tbody><tr><td>BD vacutainer blood collection set</td><td>Becton Dickinson</td><td>367286</td><td></td></tr><tr><td>BD vacuitainer K2E 5.5 mg plus blood collection tubes -3.0 mL</td><td>Becton Dickinson</td><td>7128959</td><td></td></tr><tr><td>Phospahate Buffered Saline (PBS)</td><td>Lonza</td><td>17-516F</td><td></td></tr><tr><td>5ml polystyrene round bottom FACS tube (12 mm x 75 mm style)</td><td>Invitro technologies</td><td>352054</td><td></td></tr><tr><td>V450 Mouse Anti-Human CD14 Antibody [M&amp;phi;P9]</td><td>BD Biosciences</td><td>560349</td><td></td></tr><tr><td>APC Mouse monoclonal Anti-CD16 Antibody [3G8]</td><td>Abcam Australia</td><td>ab140477</td><td></td></tr><tr><td>Per CP Anti-Human HLA-DR Antibody [L243]</td><td>BioLegend</td><td>307628</td><td></td></tr><tr><td>PE Mouse IgG1 &amp;kappa; Isotype Control [MOPC-21]</td><td>BD Biosciences</td><td>555749</td><td>Lot # 4283901</td></tr><tr><td>PE Mouse Anti-Human CD163 Antibody [GHI/61]</td><td>BD Biosciences</td><td>556018</td><td>Lot # 2335626</td></tr><tr><td>PE Mouse Anti-Human CD64 Antibody [10.1]</td><td>BD Biosciences</td><td>558592</td><td>Lot # 36768</td></tr><tr><td>PE Mouse Anti-Human CD86 Antibody [2331 (FUN-1)]</td><td>BD Biosciences</td><td>555658</td><td>Lot # 3109766</td></tr><tr><td>PE Mouse Anti-Human CD11b/Mac-1 Antibody [ ICRF44]</td><td>BD Biosciences</td><td>561001</td><td>Lot # 3228959</td></tr><tr><td>PE Mouse IgG2A Isotype control [20102]</td><td>R&amp;amp;D Systems</td><td>IC003P</td><td>Lot # 1212031</td></tr><tr><td>PE Human TNF RII/TNFRSF1B Antibody [22235]</td><td>R&amp;amp;D Systems</td><td>FAB226P</td><td>Lot # 612051</td></tr><tr><td>PE Mouse IgG1, &amp;kappa; Isotype Control [MOPC-21]</td><td>BioLegend</td><td>400112</td><td>Lot # B220359</td></tr><tr><td>PE Anti-human CD93 Antibody [VIMD2]</td><td>BioLegend</td><td>336107</td><td>Lot # B143544</td></tr><tr><td>PE Mouse Anti-Human CD3 Antibody [SK7]</td><td>BD Biosciences</td><td>347347</td><td>Lot # 3010929</td></tr><tr><td>PE Mouse Anti-Human CD19 Antibody [HIB19]</td><td>BD Biosciences</td><td>561741</td><td>Lot # 2307721</td></tr><tr><td>PE Mouse Anti-Human CD56 Antibody [B159]</td><td>BD Biosciences</td><td>561903</td><td>Lot # 3011796</td></tr><tr><td>PE Anti-human CD66b Antibody [G10F5]</td><td>BioLegend</td><td>305105</td><td>Lot # B154037</td></tr><tr><td>OptiLyse C</td><td>Beckman Coulter</td><td>A11895</td><td></td></tr><tr><td>Formaldehyde solution 37%</td><td>Sigma</td><td>F1635</td><td></td></tr><tr><td>BD FACSCanto II Flow Cytometer</td><td>BD Biosciences</td><td></td><td></td></tr><tr><td>Automatic haematology analyser, XT-1800i</td><td>Sysmex</td><td></td><td></td></tr><tr><td>Centrifuge GS-6R</td><td>Beckman</td><td></td><td></td></tr><tr><td>Flowjo software v10.0.7</td><td>Tree Star Inc</td><td></td><td></td></tr></tbody></table>

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.

The monocyte gating strategy and flow cytometry analysis used here (Figure 1) successfully gated the monocyte subsets and revealed their relative proportions. The proportions (for this sample) were calculated as 88.1% classicals, 4.33% intermediates, and 7.49% non-classicals. These subset gates were not contaminated with B cells, T cells, neutrophils or NK cells, which was confirmed with markers CD19, CD3, CD56, and CD66b, respectively. By assessing the relative position of other populations, it is clear that the T cells and neutrophils fall well outside the monocyte &#34;&#9488;&#34; shape on a CD16/CD14 plot (Figure 2A and 2D). However, both the NK cells and B cell populations overlapped with the non-classical monocyte population (Figure 2B and 2C). The steps of the gating strategy (Figure 1D and 1E) were confirmed to exclude the NK cells (Figure 3A) and B cells (Figure 3B). Although the B cell population included a small portion of non-classical monocytes, the amount was negligible.
Having gated the subsets, the degree to which they expressed different surface markers, M1 (CD64, CD86, and CD120b) and M2 (CD163, CD11b, and CD93) was assessed. The markers showed positive expression compared to their corresponding isotype controls, as seen by the shift of the histograms (Figure 4). The median of the markers was greater than that of the isotype controls.
Application of this gating strategy to a blood sample, which was split into four tubes, stained and analyzed separately, yields comparable results between tubes (Table 4).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57941/57941fig1.jpg" /> 
Figure 1: Representative monocyte gating strategy in whole human blood. (A) FSC(A) vs. FSC(H) plot: Gating the cells that have an equal area and height, thus removing clumps (greater FSC(A) relative to FSC(H)) and debris (very low FSC) K= 1000. (B) FSC(A) vs. SSC(A) plot: Broad selection of monocytes based on their SSC/FSC properties. (C) CD16 vs. CD14 plot: Gating to select monocytes based on their characteristic &#34;&#9488;&#34; shape. (D) CD16 vs. HLA-DR plot: Gating to select HLA-DR positive cells and remove NK cells. (E) CD14 vs. HLA-DR: Gating to exclude the B cells (HLA-DR high/CD14 low) from the monocytes. (F) Selected monocytes redisplayed on CD16 vs. CD14 plot to gate the monocyte subsets. For A-F color represents cell density with blue and green indicating low density, red and orange indicating high density, and orange indicating mid-range density. <a href="https://www.jove.com/files/ftp_upload/57941/57941fig1large.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/57941/57941fig2.jpg" /> 
Figure 2: Validation of gating strategy by identification of potentially contaminating cells. Potentially contaminating cells are identified by markers (A) T cells (CD3), (B) B cells (CD19), (C) NK cells (CD56), and (D) neutrophils (CD66b). The left side panels show identification of each population after gating as per Figure 1A and 1B, with color representing cell density from high (red) to low (blue). The right side panels show each cell population (blue) superimposed on the final monocyte CD16/CD14 plot to reveal proximity of these populations to monocytes (red). <a href="https://www.jove.com/files/ftp_upload/57941/57941fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57941/57941fig3.jpg" /> 
Figure 3: Confirmation that gating steps remove contaminating cells. Heat maps showing the degree of expression from high (red) to low (blue) of (A) CD56 and (B) CD19. Gating steps successfully exclude cells with high CD56 (NK cells) and high CD19 (B cells). <a href="https://www.jove.com/files/ftp_upload/57941/57941fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57941/57941fig4.jpg" /> 
Figure 4: Monocyte expression of M1 (CD120b) and M2 (CD93) markers. Smoothed histograms of monocyte M1 and M2 marker expression (blue) showing clear shift from the isotype (red) for (A) classicals, (B) intermediates, and (C) non-classicals. <a href="https://www.jove.com/files/ftp_upload/57941/57941fig4large.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>Antibody</td>
			<td>Volume (50 &#181;L blood)</td>
			<td>Volume (16x) 800 &#181;L blood</td>
		</tr>
		<tr>
			<td>CD14 - V450</td>
			<td>0.75 &#181;L</td>
			<td>12 &#181;L</td>
		</tr>
		<tr>
			<td>CD16 - APC</td>
			<td>0.5 &#181;L</td>
			<td>8 &#181;L</td>
		</tr>
		<tr>
			<td>HLA-DR-PerCP</td>
			<td>0.625 &#181;L</td>
			<td>10 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 1: Antibodies for whole blood flow and master mix.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Tube</td>
			<td>PE Antibodies</td>
			<td>Code</td>
			<td>Isotype Match</td>
			<td>Stock Concentration</td>
			<td>Working Volume</td>
		</tr>
		<tr>
			<td>1</td>
			<td>IgG1</td>
			<td>BD (555749)</td>
			<td>NA</td>
			<td>1.0 &#181;g/20 &#181;L&#160;</td>
			<td>0.625 &#181;L</td>
		</tr>
		<tr>
			<td>2</td>
			<td>CD163</td>
			<td>BD (556018)</td>
			<td>BD IgG1&#160;&#160;</td>
			<td>0.125 &#181;g/20 &#181;L</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>3</td>
			<td>CD64</td>
			<td>BD (558592)</td>
			<td>BD IgG1&#160;&#160;</td>
			<td>0.06 &#181;g/20&#181;L</td>
			<td>10 &#181;L</td>
		</tr>
		<tr>
			<td>4</td>
			<td>IgG1</td>
			<td>BD (555749)</td>
			<td>NA</td>
			<td>1.0 &#181;g/20 &#181;L</td>
			<td>1.25 &#181;L</td>
		</tr>
		<tr>
			<td>5</td>
			<td>CD86</td>
			<td>BD (555658)</td>
			<td>BD IgG1&#160;&#160;</td>
			<td>1.0 &#181;g/20 &#181;L</td>
			<td>1.25 &#181;L</td>
		</tr>
		<tr>
			<td>6</td>
			<td>CD11b</td>
			<td>BD (561001)</td>
			<td>BD IgG1&#160;&#160;</td>
			<td>1.0 &#181;g/20 &#181;L</td>
			<td>1.25 &#181;L</td>
		</tr>
		<tr>
			<td>7</td>
			<td>IgG2a</td>
			<td>R &#38;D (IC003P)</td>
			<td>NA</td>
			<td>25 &#181;g/mL</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>8</td>
			<td>CD120b (TNFRII)</td>
			<td>R &#38;D (FAB226P)</td>
			<td>R &#38;D IgG2a</td>
			<td>25 &#181;g/mL</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>9</td>
			<td>IgG1</td>
			<td>BL (400112)</td>
			<td>NA</td>
			<td>0.2 mg/mL</td>
			<td>2.5 &#181;L</td>
		</tr>
		<tr>
			<td>10</td>
			<td>CD93</td>
			<td>BL (336108)</td>
			<td>BL IgG1</td>
			<td>50 &#181;g/mL</td>
			<td>1.25 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 2: M1/M2-PE Fluorochrome labeled monoclonal antibodies for whole blood flow.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Tube</td>
			<td>Antibody</td>
			<td>Working Volume</td>
		</tr>
		<tr>
			<td>11</td>
			<td>CD3</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>12</td>
			<td>CD19</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>13</td>
			<td>CD66b</td>
			<td>1.25 &#181;L</td>
		</tr>
		<tr>
			<td>14</td>
			<td>CD56</td>
			<td>5 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 3: PE Fluorochrome labeled monoclonal antibodies for lymphocytes (T cells, B cells), neutrophils, and NK cells.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Tube</td>
			<td>% Classical</td>
			<td>% Intermediate</td>
			<td>% Non-classical</td>
		</tr>
		<tr>
			<td>1</td>
			<td>84.3</td>
			<td>5.11</td>
			<td>10.1</td>
		</tr>
		<tr>
			<td>2</td>
			<td>84.2</td>
			<td>5.05</td>
			<td>10.5</td>
		</tr>
		<tr>
			<td>3</td>
			<td>84.3</td>
			<td>5.03</td>
			<td>10.7</td>
		</tr>
		<tr>
			<td>4</td>
			<td>81.6</td>
			<td>5.03</td>
			<td>12.1</td>
		</tr>
	</tbody>
</table>
Table 4: Monocyte subset proportions from one blood sample stained and analyzed separately.

Watch this Scientific Journal Video about Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis at JoVE.com

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

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 ...

characterization-human-monocyte-subsets-whole-blood-flow-cytometry

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

57.5K Views.  Westmead Hospital. 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).

Video: Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay

Cryopreservation of peripheral blood leukocytes is widely used to preserve cells for immune response evaluations in clinical trials and offers many advantages for ease and standardization of immunological assessments, but detrimental effects of this process have been observed on some cell subsets, such as granulocytes, B cells, and dendritic cells 1-3. Assaying fresh leukocytes gives a more accurate picture of the in vivo state of the cells, but is often difficult to perform in the context of large clinical trials. Fresh cell assays are dependent upon volunteer commitments and timeframes and, if time-consuming, their application can be impractical due to the working hours required of laboratory personnel. In addition, when trials are conducted at multiple centers, laboratories with the resources and training necessary to perform the assays may not be located in sufficient proximity to clinical sites. To address these issues, we have developed an 11-color antibody staining panel that can be used with Trucount tubes (Becton Dickinson; San Jose, CA) to phenotype and enumerate the major leukocyte populations within the peripheral blood, yielding more robust cell-type specific information than assays such as a complete blood count (CBC) or assays with commercially-available panels designed for Trucount tubes that stain for only a few cell types. The staining procedure is simple, requires only 100 &mu;l of fresh whole blood, and takes approximately 45 minutes, making it feasible for standard blood-processing labs to perform. It is adapted from the BD Trucount tube technical data sheet (<a href="http://www.bdbiosciences.com/external_files/is/doc/tds/Package_Inserts_CE/live/web_enabled/23-3483-06_PI_Trucount tubes_CE_EN.pdf" target="_blank">version 8/2010</a>). The staining antibody cocktail can be prepared in advance in bulk at a central assay laboratory and shipped to the site processing labs. Stained tubes can be fixed and frozen for shipment to the central assay laboratory for multicolor flow cytometry analysis. The data generated from this staining panel can be used to track changes in leukocyte concentrations over time in relation to intervention and could easily be further developed to assess activation states of specific cell types of interest. In this report, we demonstrate the procedure used by blood-processing lab technicians to perform staining on fresh whole blood and the steps to analyze these stained samples at a central assay laboratory supporting a multicenter clinical trial. The video details the procedure as it is performed in the context of a clinical trial blood draw in the HIV Vaccine Trials Network (HVTN).

In this report, we demonstrate the staining and analysis steps of a phenotyping assay performed on fresh whole blood to enumerate major innate and adaptive leukocyte populations. We emphasize considerations for performing these procedures in the context of a multicenter clinical trial.

Cryopreservation of peripheral blood leukocytes is widely used to preserve cells for immune response evaluations in clinical trials and offers many ...

An In vitro Model to Study Heterogeneity of Human Macrophage Differentiation and Polarization

Monocyte-derived macrophages represent an important cell type of the innate immune system. Mouse models studying macrophage biology suffer from the phenotypic and functional differences between murine and human monocyte-derived macrophages. Therefore, we here describe an in vitro model to generate and study primary human macrophages. Briefly, after density gradient centrifugation of peripheral blood drawn from a forearm vein, monocytes are isolated from peripheral blood mononuclear cells using negative magnetic bead isolation. These monocytes are then cultured for six days under specific conditions to induce different types of macrophage differentiation or polarization. The model is easy to use and circumvents the problems caused by species-specific differences between mouse and man. Furthermore, it is closer to the in vivo conditions than the use of immortalized cell lines. In conclusion, the model described here is suitable to study macrophage biology, identify disease mechanisms and novel therapeutic targets. Even though not fully replacing experiments with animals or human tissues obtained post mortem, the model described here allows identification and validation of disease mechanisms and therapeutic targets that may be highly relevant to various human diseases.

An In vitro Model to Study Heterogeneity of Human Macrophage Differentiation and Polarization

Monocyte-derived macrophages are important cells of the innate immune system. Here, we describe an easy to use in vitro model to generate these cells. Using gradient centrifugation, negative bead isolation and specific cell culture conditions, monocyte-derived macrophages can be generated for phenotypic and functional studies.

Monocyte-derived macrophages represent an important cell type of the innate immune system. Mouse models studying macrophage biology suffer from the ...

Collection, Isolation, and Flow Cytometric Analysis of Human Endocervical Samples

Despite the public health importance of mucosal pathogens (including HIV), relatively little is known about mucosal immunity, particularly at the female genital tract (FGT). Because heterosexual transmission now represents the dominant mechanism of HIV transmission, and given the continual spread of sexually transmitted infections (STIs), it is critical to understand the interplay between host and pathogen at the genital mucosa. The substantial gaps in knowledge around FGT immunity are partially due to the difficulty in successfully collecting and processing mucosal samples. In order to facilitate studies with sufficient sample size, collection techniques must be minimally invasive and efficient. To this end, a protocol for the collection of cervical cytobrush samples and subsequent isolation of cervical mononuclear cells (CMC) has been optimized. Using ex vivo flow cytometry-based immunophenotyping, it is possible to accurately and reliably quantify CMC lymphocyte/monocyte population frequencies and phenotypes. This technique can be coupled with the collection of cervical-vaginal lavage (CVL), which contains soluble immune mediators including cytokines, chemokines and anti-proteases, all of which can be used to determine the anti- or pro-inflammatory environment in the vagina.

The use of cytobrush sampling to collect lymphocytes and monocytes from the endocervix is a minimally invasive technique that provides samples for analysis of female genital tract immunity. In this protocol, we describe the collection of cytobrush samples and immune cell isolation for flow cytometry assays.

Despite the public health importance of mucosal pathogens (including HIV), relatively little is known about mucosal immunity, particularly at the female ...

Medicine

A Simple Flow Cytometric Method to Measure Glucose Uptake and Glucose Transporter Expression for Monocyte Subpopulations in Whole Blood

Monocytes are innate immune cells that can be activated by pathogens and inflammation associated with certain chronic inflammatory diseases. Activation of monocytes induces effector functions and a concomitant shift from oxidative to glycolytic metabolism that is accompanied by increased glucose transporter expression. This increased glycolytic metabolism is also observed for trained immunity of monocytes, a form of innate immunological memory. Although in vitro protocols examining glucose transporter expression and glucose uptake by monocytes have been described, none have been examined by multi-parametric flow cytometry in whole blood. We describe a multi-parametric flow cytometric protocol for the measurement of fluorescent glucose analog 2-NBDG uptake in whole blood by total monocytes and the classical (CD14++CD16-), intermediate (CD14++CD16+) and non-classical (CD14+CD16++) monocyte subpopulations. This method can be used to examine glucose transporter expression and glucose uptake for total monocytes and monocyte subpopulations during homeostasis and inflammatory disease, and can be easily modified to examine glucose uptake for other leukocytes and leukocyte subpopulations within blood.

Monocytes are integral components of the human innate immune system that rely on glycolytic metabolism when activated. We describe a flow cytometry protocol to measure glucose transporter expression and glucose uptake by total monocytes and monocyte subpopulations in fresh whole blood.

Monocytes are innate immune cells that can be activated by pathogens and inflammation associated with certain chronic inflammatory diseases. Activation of ...

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 ...

Generation of Human Monocyte-derived Dendritic Cells from Whole Blood

Dendritic cells (DCs) recognize foreign structures of different pathogens, such as viruses, bacteria, and fungi, via a variety of pattern recognition receptors (PRRs) expressed on their cell surface and thereby activate and regulate immunity. 
The major function of DCs is the induction of adaptive immunity in the lymph nodes by presenting antigens via MHC I and MHC II molecules to na&#239;ve T lymphocytes. Therefore, DCs have to migrate from the periphery to the lymph nodes after the recognition of pathogens at the sites of infection. For in vitro experiments or DC vaccination strategies, monocyte-derived DCs are routinely used. These cells show similarities in physiology, morphology, and function to conventional myeloid dendritic cells. They are generated by interleukin 4 (IL-4) and granulocyte-macrophage colony-stimulating factor (GM-CSF) stimulation of monocytes isolated from healthy donors. Here, we demonstrate how monocytes are isolated and stimulated from anti-coagulated human blood after peripheral blood mononuclear cell (PBMC) enrichment by density gradient centrifugation. Human monocytes are differentiated into immature DCs and are ready for experimental procedures in a non-clinical setting after 5 days of incubation.

Here, we demonstrate how monocytes are isolated by magnetic bead separation from peripheral blood mononuclear cells after density gradient centrifugation of human anti-coagulated blood. Following incubation for 5 days, human monocytes are differentiated into immature dendritic cells and are ready for experimental procedures in a non-clinical setting.

Dendritic cells (DCs) recognize foreign structures of different pathogens, such as viruses, bacteria, and fungi, via a variety of pattern recognition ...

Quantifying Human Monocyte Chemotaxis In Vitro and Murine Lymphocyte Trafficking In Vivo

Chemotaxis is migration along a specific chemical gradient1. Chemokines are chemotactic cytokines that promote cellular trafficking with anatomic and temporal specificity2. Chemotaxis is a critical function of lymphocytes and other immune cells that can be quantitatively assessed in vitro. This manuscript describes methods that permit the evaluation of chemotaxis, both in vitro and in vivo, for diverse cell types including cell lines and native cells. The in vitro, plate-based format permits the comparison of several conditions simultaneously in real-time, and can be completed within 1-4 h. In vitro assay conditions can be manipulated to introduce agonists and antagonists, as well as differentiate chemotaxis from chemokinesis, which is random movement. For in vivo trafficking assessments, immune cells can be labeled with multiple fluorescent dyes and used for adoptive transfer. The differential labeling of cells allows for mixed cell populations to be introduced into the same animal, thereby decreasing variance and reducing the number of animals required for an adequately powered experiment. Migration into lymphoid tissue occurs in as little as 1 h, and multiple tissue compartments can be sampled. Flow cytometry following tissue harvest allows for a rapid and quantitative analysis of the migratory patterns of multiple cell types.

Quantifying Human Monocyte Chemotaxis In Vitro and Murine Lymphocyte Trafficking In Vivo

Protocols for quantitative assessment of lymphocyte chemotaxis and migration are important tools for immunology research. Here, an in vitro protocol is described that permits real-time, multiplexed evaluation of cell migration, as well as a complementary in vivo technique enabling tracking of native cells to spleen.

Chemotaxis is migration along a specific chemical gradient1. Chemokines are chemotactic cytokines that promote cellular trafficking with anatomic and ...

Quantification of Monocyte Transmigration and Foam Cell Formation from Individuals with Chronic Inflammatory Conditions

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Atherosclerosis, a leading cause of CAD, is initiated by the transmigration of innate immune monocytes to inflammatory sites of deposited lipid called fatty streaks, which are present in arterial walls of medium to large arteries. The key pathogenic feature of lesions at this early stage of atherosclerosis is the maturation of monocytes which migrate into arteries to form foam cells or lipid-laden macrophages. Considerable evidence supports the hypothesis that risk of atherosclerosis is increased by chronic inflammatory conditions accompanying diseases such as rheumatoid arthritis and HIV, as well as general ageing, and that this risk is predicted by monocyte activation. While mouse models provide a good platform to investigate the role of monocytes in atherogenesis in vivo, they require genetic alteration of natural cholesterol metabolism and drastic alteration of normal mouse diets, and have limited suitability for the study of atherogenic influences of human comorbid diseases. This motivated us to develop a human in vitro model to measure the atherogenic potential of monocytes isolated from individuals with defined disease states. Currently, human in vitro models are limiting in that they evaluate monocyte transmigration and foam cell formation in isolation. Here we describe a protocol in which monocytes isolated from patient blood transmigrate across human endothelial cells into a type 1 collagen matrix, and their propensity to mature into foam cells in the presence or absence of exogenous lipid is measured. The protocol has been validated for the use of human monocytes purified from individuals with HIV infection and elderly HIV uninfected individuals. This model is versatile and allows monocyte transmigration and foam cell formation to be evaluated using either microscopy or flow cytometry as well as allowing the assessment of atherogenic factors present in serum or plasma.

We describe a protocol to measure transmigration by monocytes across human endothelial monolayers and their subsequent maturation into foam cells. This provides a versatile method to assess the atherogenic properties of monocytes isolated from people with different disease conditions and to evaluate factors in blood which may enhance this propensity.

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Atherosclerosis, a leading cause of CAD, is initiated by the ...

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Cytokines play a pivotal role in the pathogenesis of autoimmune diseases. Hence, the measurement of cytokine levels has been the focus of multiple studies in an attempt to understand the precise mechanisms that lead to the breakdown of self-tolerance and subsequent autoimmunity. Approaches thus far have been based on the study of one specific aspect of the immune system (a single or few cell types or cytokines), and do not offer a global assessment of complex autoimmune disease. While patient sera-based studies have afforded important insights into autoimmunity, they do not provide the specific cellular source of the dysregulated cytokines detected. A comprehensive single-cell approach to evaluate cytokine production in multiple immune cell subsets, within the context of &#34;intrinsic&#34; patient-specific plasma circulating factors, is described here. This approach enables monitoring of the patient-specific immune phenotype (surface markers) and function (cytokines), either in its native &#34;intrinsic pathogenic&#34; disease state, or in the presence of therapeutic agents (in vivo or ex vivo).

Here we describe a single-cell proteomic approach to evaluate immune phenotypic and functional (intracellular cytokine induction) alterations in peripheral whole blood samples, analyzed via mass cytometry.

Cytokines play a pivotal role in the pathogenesis of autoimmune diseases. Hence, the measurement of cytokine levels has been the focus of multiple studies ...

Simultaneous Study of the Recruitment of Monocyte Subpopulations Under Flow In Vitro

The recruitment of monocytes from the blood to targeted peripheral tissues is critical to the inflammatory process during tissue injury, tumor development and autoimmune diseases. This is facilitated through a process of capture from free flow onto the luminal surface of activated endothelial cells, followed by their adhesion and transendothelial migration (transmigration) into the underlying affected tissue. However, the mechanisms that support the preferential and context-dependent recruitment of monocyte subpopulations are still not fully understood. Therefore, we have developed a method that allows the recruitment of different monocyte subpopulations to be simultaneously visualized and measured under flow. This method, based on time-lapse confocal imaging, allows for the unambiguous distinction between adherent and transmigrated monocytes. Here, we describe how this method can be used to simultaneously study the recruitment cascade of pro-angiogenic and non-angiogenic monocytes in vitro. Furthermore, this method can be extended to study the different steps of recruitment of up to three monocyte populations.

Here, we present an integrated protocol that measures monocyte subpopulation trafficking under flow in vitro by use of specific surface markers and confocal fluorescence microscopy. This protocol can be used to explore sequential recruitment steps as well as to profile other leukocyte subtypes using other specific surface markers.

The recruitment of monocytes from the blood to targeted peripheral tissues is critical to the inflammatory process during tissue injury, tumor development ...

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Summary

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Chapters in this video

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay

An In vitro Model to Study Heterogeneity of Human Macrophage Differentiation and Polarization

Collection, Isolation, and Flow Cytometric Analysis of Human Endocervical Samples

A Simple Flow Cytometric Method to Measure Glucose Uptake and Glucose Transporter Expression for Monocyte Subpopulations in Whole Blood

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

Generation of Human Monocyte-derived Dendritic Cells from Whole Blood

Quantifying Human Monocyte Chemotaxis In Vitro and Murine Lymphocyte Trafficking In Vivo

Quantification of Monocyte Transmigration and Foam Cell Formation from Individuals with Chronic Inflammatory Conditions

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Simultaneous Study of the Recruitment of Monocyte Subpopulations Under Flow In Vitro