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Originally published online as doi:10.1189/jlb.0206124 on October 17, 2006

Published online before print October 17, 2006
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(Journal of Leukocyte Biology. 2007;81:328-335.)
© 2007 by Society for Leukocyte Biology

cDNA microarray analysis reveals fundamental differences in the expression profiles of primary human monocytes, monocyte-derived macrophages, and alveolar macrophages

Jiangning Li*,1, David K. Pritchard*,1,2, Xi Wang*,3, David R. Park{dagger}, Roger E. Bumgarner{ddagger}, Stephen M. Schwartz* and W. Conrad Liles*,{dagger},4

Departments of
* Pathology,
{dagger} Medicine, and
{ddagger} Microbiology, University of Washington, Seattle, Washington, USA

2Correspondence: UW Medicine–South Lake Union, 815 Mercer Street, Seattle, WA 98109-4714, USA. E-mail: dpritch{at}u.washington.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
We report the systematic use of large-scale cDNA microarrays to study the gene expression profiles of primary human peripheral blood monocytes (MONO) in comparison with in vitro-differentiated, M-CSF-induced MONO-derived macrophages (MAC) and primary human alveolar MAC (AM), obtained by bronchoalveolar lavage from the lungs of normal volunteers. These studies revealed large-scale differences in the gene expression profile between both MAC types (MAC and AM) and MONO. In addition, large differences were observed in the gene expression profiles of the two MAC types. Specifically, 21% of genes on the array (2904 out of 13,582) were differentially expressed between AM and MONO, and 2229 out of 13,583 probes were differentially expressed between MAC and AM. Our expression data show remarkable differences in gene expression between different MAC subpopulations and emphasize the heterogeneity of different MAC populations. This study underscores the need to scrutinize models of MAC biology for relevance to specific disease processes.

Key Words: differentiation • heterogeneity • gene expression


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Progeny of the monocyte (MONO)/macrophage (MAC) lineage are capable of existing in multiple differentiation states after exiting from the bone marrow. Upon entering or traversing the vessel wall, circulating MONO begin to differentiate into tissue-based MAC and/or histiocytes. Subsets of emigrated MONO undergo further differentiation into dendritic cells and tissue-resident MAC, including the characteristic MAC of the liver, the gut, the peritoneal cavity, and the lung alveolus—locations particularly sensitive to the presence of potential microbial pathogens [1 , 2 ].

Numerous studies have profiled MONO, MONO-derived MAC derived under in vitro conditions, or transformed MONO/MAC cell lines and compared the gene expression levels among respective populations [3 4 5 6 7 8 9 10 11 12 13 14 15 16 ]. However, no study has yet compared gene expression in primary resident tissue MAC to MAC differentiated in vitro or to their precursor, the peripheral blood MONO. As in vitro-derived MAC are often used as a general model for MAC, it is important to compare the expression profiles of resident tissue MAC to in vitro-derived MAC. Alveolar MAC (AM) are particularly intriguing, as they not only are the only resident tissue MAC readily accessible in relatively pure form from healthy human volunteers but also play important roles in lung disease and host defense.

We have used microarrays to profile gene expression in primary human MONO, MONO-derived MAC cultivated in vitro with M-CSF, and resident tissue-based AM isolated by bronchoalveolar lavage (BAL) from the lungs of normal, human volunteers. Our data show profound differences in gene expression, not only between MONO and their MAC progeny (MAC and AM) but also between the two types of MAC (MAC vs. AM), thereby highlighting the heterogeneity of MAC subtypes. Moreover, this study demonstrates the use of amplification techniques for expression profiling of pure cell populations isolated from reasonable volumes of blood obtained from clinical patients or normal human volunteers.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
Four healthy volunteer subjects provided the cells used in this study. Each volunteer donated blood and underwent bronchoscopy and BAL on the same day. The age, gender, and ethnicity of the individuals were as follows: 19-year-old Asian female; 30-year-old Caucasian male; 32-year-old Caucasian male; and 32-year-old Asian male. All individuals were healthy, nonsmoking, human volunteers who had fasted overnight, were taking no medications, and had experienced no recent illness at the time of the study. The Human Subjects Division (Institutional Review Board) of the University of Washington (Seattle) approved the experimental protocol.

Isolation and preparation of primary human MONO and MONO-derived MAC
Peripheral venous blood (50 mL) was drawn from each individual in 1 mM EDTA. MONO were isolated by negative immunoselection using a commercially available, bead-based immunoselection kit. RosetteSep cocktail (50 µl/mL; Kit #15028, StemCell Technologies, Inc., Vancouver, BC, Canada) was added to the blood and incubated for 20 min at room temperature. Blood was then layered on top of Ficoll-Plaque and centrifuged for 20 min at 1200 g. Suspended MONO were isolated, and platelets were removed by repeated centrifugations. Greater than 95% of isolated cells were mononuclear, and greater than 90% of these were CD14+ by flow cytometric analysis (data not shown). Approximately 1 x 106 MONO were used immediately for RNA isolation. The remainder was used for cultivation of MONO-derived MAC, which were prepared by culturing 3 x 106 purified human peripheral blood MONO with M-CSF, as described previously [17 ]. The cells were harvested on Day 7 of culture for RNA isolation.

Isolation and preparation of primary AM
Primary human AM were collected from the same volunteer subjects, who provided venous blood for MONO isolation. AM were isolated by BAL according to a standard protocol as described previously [18 ]. AM were purified by multiple washing steps. Isolated AM were >95% viable by trypan blue exclusion, and >95% had MAC-like morphology by modified Wright-Giemsa staining.

Isolation and amplification of total RNA
All cell preparations were pelleted by centrifugation before lysis. Total RNA was isolated using the Qiagen RNAeasy mini kit (Qiagen, Hilden, Germany). The amount of total RNA was quantified by measurement of OD 260 nm. The quality of the total RNA was determined by capillary electrophoresis analysis using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA). For each different cell type, 1 µg total RNA was amplified using Arcturus kit (Version A), according to the manufacturer’s instruction (Arcturus, Mountain View CA). The quantity and quality of amplified RNA were analyzed similarly to total RNA as above.

cDNA microarrays and data analysis
Aliquots of 2 µg T7 polymerase-amplified, Cy3- or Cy5-labeled, total RNA from MONO, MAC, and AM samples were hybridized to cDNA arrays containing 13,582 probes. Microarray preparation, sample labeling, hybridization, slide washing and scanning, and image quantification were performed as described previously [19 ]. All samples were hybridized to human cDNA arrays prepared at the Center for Expression Arrays (University of Washington) and then scanned with a Molecular Dynamics scanner.

Further statistical tests were performed as described in Results using Microsoft Access and Excel, as well as the Significance Analysis of Microarray (SAM) programs [20 ]. Gene annotations were generated using the SOURCE website (http://source.stanford.edu/cgi-bin/source/sourceBatchSearch). Genes associated with over-represented gene ontology (GO) terms were identified using the Expression Analysis Systematic Explorer (EASE) program (david.niaid.nih.gov/david/ease.htm [21 ]). Bonferroni post-hoc testing was performed to verify statistical significance of all genes identified, as expressed differentially by the EASE program.

Quantitative real-time RT-PCR
Two-step quantitative real-time RT-PCR of selected gene products was used to confirm the array results using a GeneAmp 5700 sequence detection system (Applied Biosystems, Foster City, CA) and the manufacturer’s protocols. PCR primers and probes were designed according to the published cDNA sequences at GenBank for four genes of interest: CX3CR1, mannose receptor C type 1 (MRC1), urokinase plasminogen activator (PLAU), and TLR2 using Primer Express software Version 2.0 (Applied Biosystems). Primers and probes were custom-synthesized by Integrated DNA Technologies (Coralville, IA). Probes were 5'-labeled with fluorescent reporter dye FAM and 3'-labeled with quencher dye TAMRA. Duplicate PCR reactions for each sample were conducted in 50 µl reaction mixtures containing Universal Master Mix (Applied Biosystems), a specific probe and primer set, and a cDNA aliquot derived from 100 ng RNA. The cycling conditions were: 2 min 50°C, 10 min 95°C, followed by 40 cycles of 15 s, 95°C, for denaturation and 1 min, 60°C, for combined annealing and extension. For analysis of mRNA levels, relative standard curves for each gene were generated by serial dilution of cDNA derived from MAC RNA irrelevant to this study. Based on the comparative threshold cycle, the standard curve, and normalization with an 18S standard, the input amount of each gene was calculated.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Identification of differentially expressed genes among peripheral blood MONO, MONO-derived MAC, and primary AM
Gene expression in MONO, MAC, and AM was examined by hybridizing Cy3- and Cy5-labeled, total RNA probes to spotted cDNA microarrays. For each comparison, gene expression changes were compared only between probes prepared from the same subject, thereby minimizing changes in gene expression caused by differences in genetic background. For each comparison, the probes prepared from the four subjects were hybridized to two microarrays containing 13,582 clones, spotted in duplicate with a dye flip. Thus, for each of the three experimental comparisons (MAC/MONO, AM/MONO, and AM/MAC), there were four paired biological replicates with each biological replicate based on four technical measurements. For each comparison, expression ratios were calculated for each gene across the 16 measurements. To be conservative, the mean of the log10 ratios for each of the technical measurements of each biological replicate was used for the expression analysis.

SAM [20 ] was used to identify differentially expressed genes (false discovery rate was ≤5% of the genes identified). Differential expression between MONO and MAC (MAC/MONO) and between the MAC groups (AM/MAC) was impressive in the number of genes showing differential expression and in the presence of individual genes with extremely high expression ratios (see Tables 1 2 3 4 5 ). For example, several genes with ≥100-fold differential expression were identified in these experiments.


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  		Table 1. Identification of Strongly, Differentially Expressed Genes

 

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  		Table 2. Twenty Clones Most Strongly Up-Regulated in Both MAC Types (MAC and AM) Versus Peripheral Blood MONO

 

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  		Table 3. Twenty Spotted Clones Most Strongly Down-Regulated in Both MAC Types (MAC and AM) Versus Peripheral Blood MONO

 

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  		Table 4. Twenty Clones Most Strongly Up-Regulated in MONO-Derived MAC Versus Resident Pulmonary AM

 

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  		Table 5. Twenty Genes Most Strongly Down-Regulated in MONO-Derived MAC Versus Resident Pulmonary AM

 
The frequency of gene expression changes in this system was also particularly impressive. We observed that 4130 of 13,582 genes were differentially expressed in the MAC/MONO comparison using the SAM algorithm. Furthermore, 2229 and 2904 genes were differentially expressed in the MAC/AM and AM/MONO comparisons, respectively.

Large-scale gene expression differences between MONO and the two MAC types, MAC and AM, were not unexpected, considering the large differences in cellular physiology between MONO and MAC. However, the similarly large number of genes differentially expressed between the two MAC types was noteworthy, not only highlighting the difference in the transcriptional profile between MONO-derived MAC and AM but also demonstrating the transcriptional heterogeneity among distinct MAC populations.

To identify the most significantly, differentially expressed genes, the analysis was restricted to genes that showed greater than twofold change consistently in all four subjects (Table 1 ). The numbers of up-regulated and down-regulated genes identified by these more stringent criteria ranged from 161 genes up-regulated in the MAC/MONO comparison to 885 genes down-regulated in the AM/MONO comparison. Plotting the log10 ratio versus log10 intensity of the genes on mean log10 ratio/average log10 intensity (MA) plots demonstrated the large number of genes showing large ratio changes in expression (Fig. 1 ), and almost all of these genes were also significantly, differentially expressed.


Figure 1
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  		Figure 1. Isolation of genes differentially expressed in peripheral blood MONO versus MONO-derived MAC and pulmonary AM. We identified genes judged to be differentially expressed using the SAM algorithm, with P ≤ .05, and which showed greater than or equal to twofold differential expression in all four subjects. A representative MA plot of log10 expression ratio versus log10 expression intensity comparing the AM and MAC hybridization is shown for one subject. Differentially expressed genes are shown in black. Note, the number of differentially expressed genes showing greater than tenfold ratios (Log10 ratio>1 or <–1).

 
One approach for validation of a set of differentially expressed genes is the use of permutation analysis. This test takes various combinations of data and asks if any combinations, other than the one being tested, produce comparable numbers of differentially expressed genes. If such arbitrary combinations can produce a large number of differences, then the apparent differences in the tested set could have occurred by chance. We created six permuted datasets consisting of four ratios by inverting two of the four ratios in the existing dataset. Repeating our selection criteria on these six permuted datasets, we found zero positive genes (Table 1) for every comparison. Collectively, these observations suggest that the number of false-positive genes in our dataset is likely to be extremely low.

Identification of MAC marker genes in the AM and MAC data
Genes (124) were consistently up-regulated in MAC types versus peripheral blood-derived MONO (AM/MONO and MAC/MONO; Table 2 ). By definition, these genes can be considered to represent MAC-specific markers. Table 2 shows the top 20 most differentially expressed of these MAC-specific marker genes. Many of the genes identified as general MAC markers have been shown previously to be differentially expressed between MONO and in vitro-derived MAC and/or have been implicated in MAC function. For example, fatty acid-binding protein 4 (FABP4), FABP5, nuclear receptor subfamily 1, group H, member 3, also known as liver X receptor {alpha}, lipoprotein lipase, CYP27A1 (sterol 27-hydroxylase) [22 23 24 25 26 27 28 ] have all been shown to function within MAC.

Genes strongly down-regulated in both MAC types, AM and MAC, when compared with MONO were also identified in this analysis (Table 3 ). Genes (111) were down-regulated by greater than or equal to twofold in all four subjects. Table 3 shows the top 20 differentially expressed, down-regulated genes. Some of these genes, such as MAP/microtubule affinity-regulating kinase 3 (also known as CTAK1) and I{kappa}B{alpha} are expressed 50- to 100-fold more strongly in MONO versus MAC and are known to function in the regulation of important intracellular signaling pathways.

Identification of genes that distinguish between MAC types
A relatively large number of genes were expressed differentially between AM and MAC (see Tables 4 and 5 ). Genes (161) were up-regulated significantly in AM compared with MAC, and 210 genes were up-regulated significantly in MAC compared with AM. Table 4 shows the 20 most strongly, differentially expressed MAC genes. For example, two transcription factors, GATA-6 and nuclear factor of activated T cells (NFAT) cells, cytoplasmic, calcineurin-dependent 3 (NFATc3), were up-regulated ~100-fold in MAC versus AM, although neither has been implicated previously in MAC function.

However, differential expression of other genes suggests differences in function between in vitro-derived MAC and tissue-based AM. Pleiotrophin is a heparin-binding, 18-kDa secretory protein, which functions to induce mitogenesis, angiogenesis, differentiation, and transformation in vitro. It is up-regulated in MAC in response to ischemic injury [29 ]. Cyp1B1 is the predominant cytochrome P450 expressed in MONO and most MAC subtypes but is reportedly not expressed in AM [30 , 31 ]. Phagocytosis is impaired in Cyp1B1–/– MAC [32 ]. CD36 is a scavenger receptor and one of the principal receptors responsible for uptake of low-density lipoprotein by MAC [33 ]. Genetic analysis has implicated CD36 in the pathogenesis of clinical disorders as diverse as insulin resistance, hypertension, and atherosclerosis. CCL3, also known as MIP-1{alpha}, is an 8-kDa chemokine, originally purified from the supernatant of endotoxin-stimulated murine MAC, which plays an important role as a chemokine in inflammation [34 ].

Table 5 shows the 20 genes most strongly up-regulated in AM versus MAC. Six out of these 20 genes are MHC class II genes, which are up-regulated 30- to 80-fold in AM versus MAC. Compared with other resident tissue MAC, AM is notable for robust expression of MHC class II genes [35 ]. Other genes of diverse function distinguished AM from MAC, including MARCO, a bacteria-binding receptor expressed in MAC subsets [36 , 37 ]. CCL18 (pulmonary and activation-regulated), also known as Scya18, PARC, DCCK1, and AMAC1, is a chemokine with 60% homology to MIP-1{alpha} expressed in AM [38 ]. CCL18 is induced specifically in MAC by alternative MAC mediators (e.g., IL-4 and glucocorticoids) and is expressed in a reciprocal pattern to MIP-1{alpha}. This observation is interesting, given that MIP-1{alpha} expression, characteristic of the classical pattern of MAC activation, was up-regulated in MAC versus AM in the present analysis. The differential gene expression observed in MAC and AM highlights the fundamental differences between these two MAC subtypes.

GO functional analysis
Functional categories of over-represented genes in MONO/MAC subpopulations were identified using statistical criteria. The EASE program is widely used to determine GO functional categories enriched in the lists of differentially expressed genes in expression microarrays [21 ]. EASE analysis was performed to identify over-represented, functional categories, followed by Fisher’s exact test for statistical significance and Bonferroni correction for multiple comparisons. Based on this analysis, the only over-represented, functional categories observed were a core set of 19 genes associated with the GO functional categories: immune response (P=1.8x10–6), defense response (P=6.5x10–6), response to biotic stimulus (P=3x10–6), and response to external stimulus (P=3.29x10–6). The genes in these GO functional categories were up-regulated in AM versus MAC. Many genes in the cluster of 19 immune-related genes identified by GO analysis have been shown to function specifically in AM. For example, cathepsin C (dipeptidyl aminopeptidase I), which is transcriptionally regulated by the IFN regulatory factor-8-binding protein, is a lysosomal protease expressed at high levels in AM [39 , 40 ]. CXCL9 (IFN-{gamma}-induced monokine) is induced in MAC by IFN-{gamma} [41 ].

Validation of microarray data with quantitative real-time RT-PCR
Quantitative real-time RT-PCR was used to confirm the accuracy of the expression microarray data. Four candidate genes of biological importance with strong differential expression in all four subjects in MONO versus AM were selected for analysis: 1) MRC1; 2) PLAU; 3) CX3CR1; and 4) TLR2. For all four subjects, the expression levels of the respective genes in MONO, MAC, and AM were determined using quantitative real-time PCR. To facilitate comparison with the expression microarray data, MAC/MONO, MAC/AM, and AM/MONO expression ratios for the four genes were calculated for each subject. The quantitative real-time PCR data confirmed the directions of differential expression for all four genes across the subjects and showed good concordance with the magnitude of expression ratios for all four subjects (Fig. 2 ).


Figure 2
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  		Figure 2. Concordance between microarray and quantitative real-time RT-PCR analysis expression ratio measurements. Real-time RT-PCR analysis was used to quantify the levels of expression of four genes in pulmonary AM and peripheral blood MONO from all four subjects. The ratio of expression of AM to MONO was calculated. To facilitate comparison of the ratios, ratios with values <1 were inverted. The concordance between the expression ratio of MONO to AM expression for the mean expression ratios across the four subjects for the microarray (red) and real-time RT-PCR analysis (blue) is shown. Three out of four genes show high concordance in their expression ratios. MRC1 is discordant, as the microarray analysis software limits expression ratios to a maximum of a 100-fold change and as MRC1 expression is essentially undetectable in peripheral blood MONO.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
This study represents the first expression profiling of a human resident tissue MAC, namely, the AM, and compares and contrasts its expression profile to that of MONO and MONO-derived MAC isolated from the same subject. Our ability to profile cells from small amounts of blood or bronchoalveolar fluid was critical to the success of this expression-profiling project. By using amplification techniques for expression profiling of pure cell populations isolated from small volumes of blood, we were able to remove human genetic variability as a factor in the analysis, and thus, we were able to identify a large number of genes that are strongly, differentially expressed between MONO and the two MAC subpopulations.

This study identified large numbers of genes up-regulated in AM compared with MAC, which has been used extensively as a model system for studying MAC biology. As shown in Tables 3 and 4 , however, the expression phenotype MAC is different from that of AM, which represents perhaps the only type of primary tissue-based MAC that can be readily isolated from normal human volunteers. The differential expression data provide intriguing clues about why this difference may exist. Two transcription factors, NFATc3 and GATA-6, were highly up-regulated in MAC. NFATc3 is particularly interesting, as it has been implicated in the regulation of the expression of a wide variety of cytokine genes in T cells [42 ]. Conceivably, it may play a similar role in MAC.

In summary, our data show remarkable differences in gene expression between different MAC subpopulations. This is not a surprise. For example, a recent review by Gordon suggested that the concept of the tissue MAC as a single, discrete cell type is overly simplistic, because of the heterogeneity between resident tissue MAC from different sources [43 ]. The results from this study support this concept and highlight the differences between in vitro-derived MAC and AM. Our observations also demonstrate the limitations of in vitro-derived MAC as models and suggest the need for greater molecular characterization of MAC subtypes. Furthermore, this study demonstrates the use of amplification techniques for expression profiling of pure cell populations isolated from reasonable volumes of blood obtained from clinical patients or normal human volunteers.


   ACKNOWLEDGEMENTS
 
This study was supported in part by HL62995 (W. C. L.) from the National Institutes of Health. The array data on which this study is based have been submitted to the ArrayExpress and GEO databases. The ArrayExpress accession is E-MEXP-524. The GEO accession is GSE5207. The authors thank members of the University of Washington Center for Expression Arrays who manufactured the spotted arrays used in this study.


   FOOTNOTES
 
1 These authors contributed equally to this work. Back

3 Current address: Department of Pharmacology, Cytokinetics Inc., South San Francisco, CA 94080, USA. Back

4 Current address: McLaughlin Centre for Molecular Medicine, University of Toronto/University Health Network, Toronto General Hospital, 13E 220, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada. Back

Received February 28, 2006; revised August 8, 2006; accepted August 25, 2006.


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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
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