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 x 75 mm style)</td>
			<td>Invitro technologies</td>
			<td>352054</td>
			<td></td>
		</tr>
		<tr>
			<td>V450 Mouse Anti-Human CD14 Antibody [M&#966;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 &#954; 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&#38;D Systems</td>
			<td>IC003P</td>
			<td>Lot # 1212031</td>
		</tr>
		<tr>
			<td>PE Human TNF RII/TNFRSF1B Antibody [22235]</td>
			<td>R&#38;D Systems</td>
			<td>FAB226P</td>
			<td>Lot # 612051</td>
		</tr>
		<tr>
			<td>PE Mouse IgG1, &#954; 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

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Immunology and Infection

57.4K 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

Human In Vitro Suppression as Screening Tool for the Recognition of an Early State of Immune Imbalance

Regulatory T cells (Tregs) are critical mediators of immune tolerance to self-antigens. In addition, they are crucial regulators of the immune response following an infection. Despite efforts to identify unique surface marker on Tregs, the only unique feature is their ability to suppress the proliferation and function of effector T cells. While it is clear that only in vitro assays can be used in assessing human Treg function, this becomes problematic when assessing the results from cross-sectional studies where healthy cells and cells isolated from subjects with autoimmune diseases (like Type 1 Diabetes-T1D) need to be compared. There is a great variability among laboratories in the number and type of responder T cells, nature and strength of stimulation, Treg:responder ratios and the number and type of antigen-presenting cells (APC) used in human in vitro suppression assays. This variability makes comparison between studies measuring Treg function difficult. The Treg field needs a standardized suppression assay that will work well with both healthy subjects and those with autoimmune diseases. We have developed an in vitro suppression assay that shows very little intra-assay variability in the stimulation of T cells isolated from healthy volunteers compared to subjects with underlying autoimmune destruction of pancreatic &beta;-cells. The main goal of this piece is to describe an in vitro human suppression assay that allows comparison between different subject groups. Additionally, this assay has the potential to delineate a small loss in nTreg function and anticipate further loss in the future, thus identifying subjects who could benefit from preventive immunomodulatory therapy1. Below, we provide thorough description of the steps involved in this procedure. We hope to contribute to the standardization of the in vitro suppression assay used to measure Treg function. In addition, we offer this assay as a tool to recognize an early state of immune imbalance and a potential functional biomarker for T1D.

Human In Vitro Suppression as Screening Tool for the Recognition of an Early State of Immune Imbalance

Tregs are potent suppressors of the immune system. There is a lack of unique surface markers to define them, hence, definitions of Tregs are primarily functional. Here we describe an optimized in vitro assay capable of identifying immune imbalance in subjects at risk to develop T1D.

Regulatory T cells (Tregs) are critical mediators of immune tolerance to self-antigens. In addition, they are crucial regulators of the immune response ...

Culture of Macrophage Colony-stimulating Factor Differentiated Human Monocyte-derived Macrophages

A protocol is presented for cell culture of macrophage colony-stimulating factor (M-CSF) differentiated human monocyte-derived macrophages. For initiation of experiments, fresh or frozen monocytes are cultured in flasks for 1 week with M-CSF to induce their differentiation into macrophages. Then, the macrophages can be harvested and seeded into culture wells at required cell densities for carrying out experiments. The use of defined numbers of macrophages rather than defined numbers of monocytes to initiate macrophage cultures for experiments yields macrophage cultures in which the desired cell density can be more consistently attained. Use of cryopreserved monocytes reduces dependency on donor availability and produces more homogeneous macrophage cultures.

A protocol is presented for cell culture of macrophage colony-stimulating factor (M-CSF) differentiated human monocyte-derived macrophages. The protocol utilizes cryopreservation of monocytes coupled with their bulk differentiation into macrophages. Then harvested macrophages can then be seeded into culture wells at required cell densities for carrying out experiments.

A protocol is presented for cell culture of macrophage colony-stimulating factor (M-CSF) differentiated human monocyte-derived macrophages. For initiation ...

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 ...

Phenotypic Characterization of Macrophages from Rat Kidney by Flow Cytometry

There is increasing evidence suggesting the important role of inflammation and, subsequently, macrophages in the development and progression of renal disease. Macrophages are heterogeneous cells that have been implicated in kidney injury. Macrophages may be classified into two different phenotypes: classically activated macrophages (M1 macrophages), that release pro-inflammatory cytokines and promote fibrosis; and alternatively activated macrophages (M2 macrophages) that are associated with immunoregulatory and tissue-remodeling functions. These macrophage phenotypes need to be discriminated and analyzed to determine their contribution to renal injury. However, there are scarce studies reporting consistent phenotypic and functional information about macrophage subtypes in inflammatory renal disease models, especially in rats. This fact may be related to the limited macrophage markers used in rats, contrary to mice. Therefore, novel strategies are necessary to quantify and characterize the renal content of these infiltrating cells in a reliable way. This manuscript details a protocol for kidney digestion and further phenotypic and quantitative analysis of macrophages from rat kidneys by flow cytometry. Briefly, kidneys were incubated with collagenase and total macrophages were identified according to the dual presence of CD45 (leukocytes common antigen) and CD68 (PAN macrophage marker) in live cells.This was followed by surface staining of CD86 (M1 marker) and CD163 (M2 marker). Rat peritoneal macrophages were used as positive control for macrophage marker detection by flow cytometry. Our protocol resulted in low cellular mortality and allowed characterization of different intracellular and surface protein markers, thus limiting the loss of cellular integrity observed in other protocols. Moreover, this procedure allows the use of macrophages for further techniques, including cell sorting and mRNA or protein expression studies, among others.

This manuscript describes a detailed protocol for phenotypic and quantitative analysis of resident macrophages from rat kidneys by flow cytometry. The resulting stained cells can be also used for other applications, including cell sorting, gene expression analysis or functional studies, thus increasing the information obtained in the experimental model.

There is increasing evidence suggesting the important role of inflammation and, subsequently, macrophages in the development and progression of renal ...

Flow Cytometric Analysis of Natural Killer Cell Lytic Activity in Human Whole Blood

NK cell cytotoxicity is a widely used measure to determine the effect of outside intervention on NK cell function. However, the accuracy and reproducibility of this assay can be considered unstable, either because of user&#39;s errors or because of the sensitivity of NK cells to experimental manipulation. To eliminate these issues, a workflow that reduces them to a minimum was established and is presented here. To illustrate, we obtained blood samples, at various time points, from runners (n = 4) that were submitted to an intense bout of exercise. First, NK cells are simultaneously identified and isolated through CD56 tagging and magnetic-based sorting, directly from whole blood and from as little as one milliliter. The sorted NK cells are removed of any reagent or capping antibodies. They can be counted in order to establish an accurate NK cell count per milliliter of blood. Secondly, the sorted NK cells (effectors cells or E) can be mixed with 3,3&#39;-Diotadecyloxacarbocyanine Perchlorate (DiO) tagged K562 cells (target cells or T) at an assay-optimal 1:5 T:E ratio, and analyzed using an imaging flow-cytometer that allows for the visualization of each event and the elimination of any false positive or false negatives (such as doublets or effector cells). This workflow can be completed in about 4 h, and allows for very stable results even when working with human samples. When available, research teams can test several experimental interventions in human subjects, and compare measurements across several time points without compromising the data&#39;s integrity.

This work describes an advanced workflow for the accurate and fast determination of NK (Natural Killer) cell count and NK cell cytotoxicity in human blood samples.

NK cell cytotoxicity is a widely used measure to determine the effect of outside intervention on NK cell function. However, the accuracy 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 ...

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry

Numerous studies have demonstrated the role of immune cells, in particular macrophages, in central nervous system (CNS) pathologies. There are two main macrophage populations in the CNS: (i) the microglia, which are the resident macrophages of the CNS and are derived from yolk sac progenitors during embryogenesis, and (ii) the monocyte-derived macrophages (MDM), which can infiltrate the CNS during disease and are derived from bone marrow progenitors. The roles of each macrophage subpopulation differ depending on the pathology being studied. Furthermore, there is no consensus on the histological markers or the distinguishing criteria used for these macrophage subpopulations. However, the analysis of the expression profiles of the CD11b and CD45 markers by flow cytometry allows us to distinguish the microglia (CD11b+CD45med) from the MDM (CD11b+CD45high). In this protocol, we show that the density gradient centrifugation and the flow cytometry analysis can be used to characterize these CNS macrophage subpopulations, and to study several markers of interest expressed by these cells as we recently published. Thus, this technique can further our understanding of the role of macrophages in mouse models of neurological diseases and can also be used to evaluate drug effects on these cells.

This protocol provides an analysis of the macrophage subpopulations in the adult mouse central nervous system by flow cytometry and is helpful for the study of multiple markers expressed by these cells.

Numerous studies have demonstrated the role of immune cells, in particular macrophages, in central nervous system (CNS) pathologies. There are two main ...

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and their important role in lung development and function, as well as their lung-localized responses to infection and inflammation. To date, no unified method for identification, isolation, and handling of alveolar macrophages from humans and mice exists. Such a method is needed for studies on these important innate immune cells in various experimental settings. The method described here, which can be easily adopted by any laboratory, is a simplified approach to harvesting alveolar macrophages from bronchoalveolar lavage fluid or from lung tissue and maintaining them in vitro. Because alveolar macrophages primarily occur as adherent cells in the alveoli, the focus of this method is on dislodging them prior to harvest and identification. The lung is a highly vascularized organ, and various cell types of myeloid and lymphoid origin inhabit, interact, and are influenced by the lung microenvironment. By using the set of surface markers described here, researchers can easily and unambiguously distinguish alveolar macrophages from other leukocytes, and purify them for downstream applications. The culture method developed herein supports both human and mouse alveolar macrophages for in vitro growth, and is compatible with cellular and molecular studies.

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

This communication describes methodologies for isolation and culture of alveolar macrophages from humans and murine models for experimental purposes.

Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and ...

Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry

Immune cell characterization heavily relies on multicolor flow cytometry to identify subpopulations based on differential expression of surface markers. Setup of a classic multicolor panel requires high-end instruments, custom labeled antibodies, and careful study design to minimize spectral overlap. We developed a multiparametric analysis to identify major human immune populations (CD4+ and CD8+ T cells, &#947;&#948; T cells, B cells, NK cells and monocytes) in peripheral blood by combining seven lineage markers using only two fluorochromes. Our strategy is based on the observation that lineage markers are constantly expressed in a unique combination by each cell population. Combining this information with a careful titration of the antibodies allows investigators to record five additional markers, expanding the optical limit of most flow cytometers. Head-to-head comparison demonstrated that the vast majority of immune cell populations in peripheral blood can be characterized with comparable accuracy between our method and the classic &#34;one fluorochrome-one marker approach&#34;, although the latter is still more precise for identifying populations such as NKT cells and &#947;&#948; T cells. Combining seven markers using two fluorochromes allows for the analysis of complex immune cell populations and clinical samples on affordable 6-10 fluorochrome flow cytometers, and even on 2-3 fluorochrome field instruments in areas with limited resources. High-end instruments can also benefit from this approach by using extra fluorochromes to accomplish deeper flow cytometry analysis. This approach is also very well suited for screening several cell populations in the case of clinical samples with limited number of cells.

Here, we present a flow cytometric protocol to identify CD4+ and CD8+ T cells, &#947;&#948; T cells, B cells, NK cells and monocytes in human peripheral blood by using only two fluorochromes instead of seven. With this approach, five additional markers can be recorded on most flow cytometers.

Immune cell characterization heavily relies on multicolor flow cytometry to identify subpopulations based on differential expression of surface markers. ...

Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells

In this article, we present a protocol that is optimized to preserve neutrophil-lineage cells in fresh BM for whole BM CyTOF analysis. We utilized a myeloid-biased 39-antibody CyTOF panel to evaluate the hematopoietic system with a focus on the neutrophil-lineage cells by using this protocol. The CyTOF result was analyzed with an open-resource dimensional reduction algorithm, viSNE, and the data was presented to demonstrate the outcome of this protocol. We have discovered new neutrophil-lineage cell populations based on this protocol. This protocol of fresh whole BM preparation may be used for 1), CyTOF analysis to discover unidentified cell populations from whole BM, 2), investigating whole BM defects for patients with blood disorders such as leukemia, 3), assisting optimization of fluorescence-activated flow cytometry protocols that utilize fresh whole BM.

Here, we present a protocol to process fresh bone marrow (BM) isolated from mouse or human for high-dimensional mass cytometry (Cytometry by Time-Of-Flight, CyTOF) analysis of neutrophil-lineage cells.

In this article, we present a protocol that is optimized to preserve neutrophil-lineage cells in fresh BM for whole BM CyTOF analysis. We utilized a ...

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

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Human In Vitro Suppression as Screening Tool for the Recognition of an Early State of Immune Imbalance

Culture of Macrophage Colony-stimulating Factor Differentiated Human Monocyte-derived Macrophages

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

Phenotypic Characterization of Macrophages from Rat Kidney by Flow Cytometry

Flow Cytometric Analysis of Natural Killer Cell Lytic Activity in Human Whole Blood

Generation of Human Monocyte-derived Dendritic Cells from Whole Blood

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry

Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells