Identification of Novel Functional Differences in Monocyte Subsets Using Proteomic and Transcriptomic Methods Changqing Zhao,†,# Huoming Zhang,‡,# Wing-Cheong Wong,§ Xiaohui Sem,† Hao Han,§ Siew-Min Ong,† Yann-Chong Tan,† Wei-Hseun Yeap,† Chee-Sian Gan,‡ Kok-Quan Ng,| Mickey Boon-Chai Koh,| Philippe Kourilsky,† Siu-Kwan Sze,‡ and Siew-Cheng Wong*,† Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (ASTAR), 8A Biomedical Grove, Biopolis, Singapore 138648, School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Bioinformatic Institutes (BII), ASTAR, 30 Biopolis Street, Biopolis, Singapore 138671, and Blood Services Group, Health Sciences Authority, 11 Outram Road, Singapore, 169078 Received April 22, 2009
Human blood monocytes can be broadly divided into two distinct subsets: CD14+CD16- and CD14+/ lowCD16+ subsets. Perturbation in their proportions in the blood has been observed in several disease conditions. Although numerous phenotypic and functional differences between the two subsets have already been described, the roles contributed by each subset during homeostasis or disease conditions are still largely unclear. To uncover novel differences to aid in elucidating their functions, we perform a global analysis of the two subsets utilizing both proteomics and transcriptomics approaches. From the proteomics and transcriptomics data, the expression of 613 genes by the two subsets is detected at both the protein and mRNA levels. These 613 genes are assessed for up-regulation in each subset at the protein and mRNA levels using a cutoff fold change of g|1.5| between subsets. Proteins and mRNAs up-regulated in each subset are then mapped in silico into biological functions. This mapping reveals copious functional differences between the subsets, many of which are seen at both protein and mRNA levels. For instance, expression of genes involved in FCY receptor-mediated phagocytosis are up-regulated in the CD14+/lowCD16+ subset, while those involved in antimicrobial function are up-regulated in the CD14+CD16- subset. We uncover novel functional differences between the monocyte subsets from differences in gene expression at the protein and mRNA levels. These functional differences would provide new insights into the different roles of the two monocyte subsets in regulating innate and adaptive immune responses. Keywords: Transcriptomics • proteomics • iTRAQ • monocyte subsets • phagocytosis • antimicrobial
Introduction Monocytes originate from myelomonocytic precursors in the bone marrow.1 Upon maturation, they are released into the blood circulation where they remain for a few days before migrating into tissues and differentiate into macrophages. The major roles of monocytes are host defense against pathogens and the maintenance of normal tissue structures and functions.1 Human blood monocytes can be broadly divided into two distinct subsets based on their differential expression of surface markers CD14 and CD16. The “classical” CD14+CD16(hereafter referred to as “CD16-”) subset constitutes 80-90% * Corresponding author: Dr. Siew-Cheng Wong, Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (ASTAR), 8A, Biomedical Grove, #04-04, Immunos, Biopolis, Singapore 138648. Tel: +65 64070030. Fax: +65 64642057. E-mail: wong_siew_cheng@ immunol.a-star.edu.sg. † Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (ASTAR). # These authors contributed equally. ‡ Nanyang Technological University. § Bioinformatic Institutes (BII), ASTAR. | Blood Services Group, Health Sciences Authority.
4028 Journal of Proteome Research 2009, 8, 4028–4038 Published on Web 06/10/2009
of the total monocyte population, while the CD14+/lowCD16+ (hereafter referred to as “CD16+”) subset constitutes 10-20%.2 The CD16+ monocytes are considered to be more mature than the “classical” monocytes due to their lower expression of CD33 (a myelomonocytic stem cell antigen) and CD11b (also known as ITGAM), and a higher expression of HLA-DR (a class II antigen-presentation molecule), typical features of tissue macrophages.3 In addition, CD16+ monocytes readily express the pro-inflammatory cytokines, tumor necrosis factor alpha (TNFR), interleukin (IL)-1β, and IL-6, and fail to produce the antiinflammatory cytokine, IL-10.3 Hence, they are termed “proinflammatory” monocytes. Consistent with this concept, the proportion of these cells are expanded in numerous inflammatory conditions such as sepsis,4 HIV infection5 and autoimmune disorders.6,7 Previous studies describing functional differences between monocyte subsets were mostly inferred from the differential expression of surface markers that were well-established.2,8 Recent advances in genomic and proteomic profiling technologies, together with improvement in detection sensitivity,9,10 have provided the potential to uncover novel functional dif10.1021/pr900364p CCC: $40.75
2009 American Chemical Society
Novel Functional Differences in Monocyte Subsets
research articles
Figure 1. Monocyte subset purity and experimental setup for proteomics and transcriptomics. (A) Identification of monocyte subsets in total monocytes using surface markers CD14 and CD16. (B) Purity of isolated monocyte subsets. Percentages indicate the purity of the isolated fractions and plot is a representative of subsets isolated for all experiments performed. (C) For proteomics, each subset from three biological replicates were pooled and labeled with iTRAQ tags as depicted. Digested peptide mixtures were further fractionated by SCX and RP chromatography and identified with MS. For transcriptomics, each subset from four biological replicates was processed separately for microarray analysis.
ferences between monocyte subsets. For instance, a global gene expression analysis of human monocyte subsets recently performed by Mobley et al. reported several differences between the two subsets not previously described, substantiating the advantage of such global approach.11 However, it is wellestablished that changes in mRNA level may not always necessarily equate to an alteration in the protein expression because of events such as post-transcriptional regulation including events such as mRNA splicing, editing, transport and degradation12 as well as post-translational regulation. Indeed, a comparison of transcriptomics and proteomics data carried out by several groups showed that the two methods do not always fully correlate.13,14 We, therefore, employed a combined proteomic and transcriptomic approach to discover novel functional differences between the monocyte subsets and obtained a comprehensive list of differentially expressed genes at both the protein and mRNA levels.
Materials and Methods Antibodies. The antibodies (Abs) used are CD14 (61D3), CD16 (3G8) (Biolegend, San Diego, CA), MPO (392105) (R & D
Systems, Minneapolis, MN), CTSG (19C3), S100A9 (Abcam, Cambridge, U.K.), Arp2 (ZZ8), Arp3 (A-1), HCK (3D12E10), LYN (LYN-01), LYZ (BGN/06/961) (Santa Cruz Biotechnology, Santa Cruz, CA). Cells. Peripheral blood mononuclear cells (PBMCs) were isolated from buffy coats obtained from Health Sciences Authority, Singapore using Ficoll-Hypaque density gradient centrifugation. The isolation of CD16- and CD16+ monocyte subsets by magnetic cell sorting was performed using the CD16 monocyte isolation kit (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s instruction with some modifications. Briefly, after magnetic depletion of NK cells and neutrophils with anti-CD56 and anti-CD15 microbeads, the CD16+ monocytes were positively purified using anti-CD16 microbeads. The CD16- monocytes were isolated from the negative fraction with anti-CD14 microbeads. For isolation by fluorescence activated cell sorting (FACS), total monocytes were isolated with anti-CD14 microbeads before they were stained with fluorochrome-conjugated anti-CD14 and anti-CD16 Abs and sorted according to the gating shown in Figure 1A. Purity of the monocyte subsets obtained was Journal of Proteome Research • Vol. 8, No. 8, 2009 4029
research articles examined by flow cytometry with fluorochrome-conjugated anti-CD14 and anti-CD16 Abs. For transcriptomics analysis, monocyte subsets isolated from 4 different individuals were processed and used separately. However, monocyte subsets isolated from a single donor do not recover sufficient CD16+ monocytes for proteomics; hence, subsets obtained from three different individuals were pooled for proteomics analysis. Samples obtained for validations by real-time PCR and Western blotting were obtained from donors different to those used in the proteomics and transcriptomics experiments. Flow Cytometry. For cell surface stainings, fluorochromeconjugated antibodies were incubated with cells for 15 min at 4 °C. Appropriate isotype antibodies were used as negative controls. All data were analyzed using FlowJo software (Tree Star, Inc., Ashland, OR). Protein Preparation and Quantification. Monocyte subsets isolated from three healthy donors were combined and washed three times with PBS and then resuspended in 200 µL of lysis buffer (0.5 M triethylammonium bicarbonate (TEAB) and 1% SDS) at 4 °C. To ensure complete lysis, the cell lysate was subjected to intermittent sonication using a Vibra Cell high intensity ultrasonic processor (Jencon, Leighton Buzzard, Bedfordshire, U.K.). The remaining unbroken cells and debris were removed by centrifugation at 12 000g at 4 °C for 10 min. Protein concentration of cleared lysates was then determined by 2-D Quant Kit (GE Healthcare, Milwaukee, WI) according to the manufacturer’s instructions. Protein Digestion and iTRAQ Labeling. Approximately 100 µg of total protein from each monocyte subset, that is, CD16and CD16+ was required. Technical replicates for each subset were set up since there are four isobaric tags but only two subsets. The proteins were reduced with 5 mM tris-carboxyethyl phosphine hydrochloride (TCEP) for 1 h at 37 °C, alkylated with 10 mM methylethanethiosulfonate (MMTS) for 20 min at room temperature (RT), and then diluted 10 times with deionized water prior to digestion with trypsin (Promega, Madison, WI) overnight at 37 °C at a 1 part trypsin to 50 part protein mass ratio and then dried using a Speedvac (Thermo Electron, Waltham, MA). Digested proteins were labeled with iTRAQ reagents (Applied Biosystems, Framingham, MA) according to the manufacturer’s protocol. Briefly, peptides were reconstituted in 30 µL of dissociation buffer (0.5 M TEAB) and mixed with 70 µL of ethanol-suspended iTRAQ reagents (one iTRAQ reporter tag per protein sample). The samples were labeled as followed: CD16- subset was labeled with reporter tags 114 and 116; and CD16+ subset was labeled with reporter tags 115 and 117. Labeling reactions were carried out at RT for 1 h before all four samples were mixed into a single tube and dried using a Speedvac. Strong Cation Exchange (SCX) Fractionation. The combined iTRAQ-labeled samples were reconstituted with 200 µL of buffer A (10 mM KH2PO4, pH 3.0, 25% (v/v) acetonitrile), and loaded into a PolySULFOETHYL A column (200 mm length × 4.6 mm i.d., 200-Å pore size, 5 µm particle size) (PolyLC, Columbia, MD) on a prominence HPLC system (Shimadzu, Kyoto, Japan). The sample was fractionated using a gradient of 100% buffer A for 5 min, 5-30% buffer B (10 mM KH2PO4, pH 3.0, 500 mM KCl and 25% (v/v) acetonitrile) for 40 min, 30-100% buffer B for 5 min, and finally 100% buffer B for 5 min, at a constant flow rate of 1 mL/min for a total of 60 min. The eluted fractions were monitored through a UV detector at 214 nm wavelength. Fractions were collected at 1-min intervals and consecutive fractions with low peak intensity were com4030
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Zhao et al. bined. Finally, a total of 20 fractions were obtained and dried in a Speedvac. Each fraction was reconstituted in 0.1% trifluoroacetic acid and desalted using a Sep-Pak C-18 SPE cartridge (Waters, Milford, MA). Desalted samples were dried in a Speedvac and stored at -20 °C. Each dried fraction was reconstituted in 60 µL of 0.1% formic acid and 2% acetonitrile just before mass spectrometric analysis. Mass Spectrometric Analysis Using QSTAR and Data Analysis. The labeled sample was analyzed three times using a QSTAR Elite mass spectrometer (Applied Biosystems; MDSSciex), coupled with an online Tempo nano MDLC system (Applied Biosystems). For each run, 10 µL of labeled peptide mixture from each fraction was injected and separated on a home-packed nanobored C18 column with a picofrit nanospray tip (75 µm i.d. × 15 cm, 5 µm particles) (New Objective, Wubrun, MA). The separation was performed at a constant flow rate of 0.3 µL/min with a 120 min gradient. The mass spectrometer was set to perform data acquisition in the positive ion mode, with a selected mass range of 300-2000 m/z. Peptides with +2 to +4 charge states were selected for MS/MS and the time of summation of MS/MS events was set to 2 s. The three most abundantly charged peptides above a 5 count threshold were selected for MS/MS and dynamically excluded for 30 s with (30 mmu mass tolerance. Peptide quantification and protein identification were performed using ProteinPilot software v2.0.1 (Applied Biosystems) by searching the combined data from the 3 runs against the International Protein Index (IPI) human database (IPI_human version 3.34.fasta, including 69 164 sequences and 29 064 824 residues). The Paragon algorithm in ProteinPilot software was used whereby trypsin was selected as the digestion agent and cysteine modification of methylethanethiosulfonate. The search also allows for the possibilities of more than 80 biological modifications using the BLOSUM 62 matrix. Only peptides with at least 70% confidence were taken for protein identification and quantification.15 All proteins used for downstream analysis should match to at least two unique peptides with iTRAQ ratios, and at least one of them must have an expectation value of less than 0.05. Finally, a concatenated target-decoy database search strategy was also performed to estimate the rate of false positives and it was determined to be 95% was consistently obtained for the isolated subset populations with both purification methods (Figure 1B). Contaminating cells such as neutrophils and lymphocytes were usually
