- Research article
- Open Access
- Open Peer Review
RNA sequencing from human neutrophils reveals distinct transcriptional differences associated with chronic inflammatory states
- Kaiyu Jiang†1,
- Xiaoyun Sun†2,
- Yanmin Chen1,
- Yufeng Shen2, 3Email author and
- James N. Jarvis1Email author
© Jiang et al. 2015
- Received: 17 March 2015
- Accepted: 11 August 2015
- Published: 27 August 2015
The transcriptional complexity of mammalian cells suggests that they have broad abilities to respond to specific environmental stimuli and physiologic contexts. These abilities were not apparent a priori from the structure of mammalian genomes, but have been identified through detailed transcriptome analyses. In this study, we examined the transcriptomes of cells of the innate immune system, human neutrophils, using RNA sequencing (RNAseq).
We sequenced poly-A RNA from nine individual samples corresponding to specific phenotypes: three children with active, untreated juvenile idiopathic arthritis (JIA)(AD), three children with the same disease whose disease was inactive on medication (CRM), and three children with cystic fibrosis (CF).
We demonstrate that transcriptomes of neutrophils, typically considered non-specific in their responses and functions, display considerable specificity in their transcriptional repertoires dependent on the pathologic context, and included genes, gene isoforms, and long non-coding RNA transcripts. Furthermore, despite the small sample numbers, these findings demonstrate the potential of RNAseq approaches to biomarker development in rheumatic diseases.
These data demonstrate the capacity of cells previously considered non-specific in function to adapt their transcriptomes to specific biologic contexts. These data also provide insight into previously unrecognized pathological pathways and show considerable promise for elucidating disease and disease-state specific regulatory networks.
- Cystic Fibrosis
- Juvenile Idiopathic Arthritis
- Ingenuity Pathway Analysis
- Juvenile Idiopathic Arthritis Patient
- lncRNA Expression
Neutrophils are the most common leukocytes in the human circulation and an important sentinel for recognizing invading micro-organisms and tissue damage. Thus, they are an important component of the acute response to infection and tissue injury. However, in recent years, we have also demonstrated that neutrophils show transcriptional aberrations in chronic childhood inflammatory diseases, including juvenile idiopathic arthritis (JIA)  and juvenile dermatomysositis . In JIA, these transcriptional aberrations do not correct with therapy  and are associated with specific perturbations in cellular metabolic function . Thus, in addition to their role in acute infectious and inflammatory disease, neutrophils appear to play important roles in chronic, indolent human inflammatory diseases.
The gene expression data used to elucidate the above findings were generated using conventional hybridization-based gene microarrays. The limits of hybridization-based microarrays are well documented . Furthermore, hybridization-based arrays fail to capture the full complexity of the transcriptome, including novel alternatively spliced isoforms and non-coding RNAs. Therefore, gene microarrays have serious limits from the standpoint of understanding the transcriptional-rewiring  that very likely underlies many complex human diseases.
RNA sequencing techniques carry the promise of revolutionizing our understanding of the transcription processes that underlie phenotypes . As data from projects like ENCODE  reveal the complexities of the transcriptome in eukaryotic cells, it is becoming clear that, in order to fully understand human pathological cellular networks, we are going to need more detail of the transcriptional events that underlie disease phenotypes.
Neutrophils are a particularly challenging cell with which to work. The presence of endonucleases within human neutrophils, a part of the host defense against bacteria , presents particular challenges to preparing high-quality nucleic acid for sequencing studies. Neutrophils are thus conspicuously absent from both the ENCODE and Roadmap Epigenomics data sets. The studies we report here were undertaken to determine the specificity of neutrophil transcriptomes to specific human illnesses or disease states, a prerequisite for biomarker development, by examining specific phenotypes that show subtle differences from one another.
Patients and patient samples
Neutrophils were collected from nine children after informed consent was obtained from their parents according to a protocol approved by the University of Oklahoma Health Sciences Center Institutional Review Board. Three of the samples were from children (ages 5–10 years, all girls) with newly-diagnosed, untreated polyarticular juvenile idiopathic articular arthritis (JIA). Samples were also obtained from 3 patients; also girls aged 5–10, who fit criteria for clinical remission on medication (CRM). That is, these children had normal physical exams, no symptoms of arthritis (morning stiffness, gait disturbance, fatigue) and normal laboratory studies (complete blood counts, erythrocyte sedimentation rate) and had maintained this state for at least 6 continuous months. In addition, a control population consisting of 3 children with cystic fibrosis (CF) (ages 6–21 years, all boys) was also studied. The latter group is an important and seldom used-control; children with CF have chronic, indolent inflammation in the lung, and thus allow us to discern disease-specific characteristics in JIA from those that might be seen in any chronic, sub-acute inflammatory state. Children with CF were seen during routine follow-up and were stable from the standpoint of pulmonary symptoms at the time they were studied.
Whole blood was drawn into 10 mL CPT tubes (Becton Dickinson, Franklin Lakes, NJ), which is an evacuated blood collection tube system containing sodium citrate anticoagulant and blood separation media composed of a thixotropic polyester gel and a FICOLL™ Hypaque™ solution. Cell separation procedures were started within 1 h from the time the specimens were drawn. Neutrophils were separated by density-gradient centrifugation at 1,700× g for 20 min. After removing red cells from neutrophils by hypotonic lysis, neutrophils were then immediately placed in TRIzol® reagent (Invitrogen, Carlsbad, CA) and stored at −80 °C until used for RNA isolation. Cells prepared in this fashion are more than 98 % CD66b + by flow cytometry and contain no contaminating CD14+ cells, as previously reported . Thus, although these cell preparations contained small numbers of other granulocytes, they will be referred to here as “neutrophils” for brevity and convenience.
RNA isolation and sequencing
Total RNA was extracted using Trizol® reagent according to manufacturer’s directions. RNA was further purified using RNeasy MiniElute Cleanup kit including a DNase digest according to the manufacturer’s instructions (QIAGEN, Valencia, CA). RNA was quantified spectrophotometrically (Nanodrop, Thermo Scientific, Wilmington, DE) and assessed for quality by capillary gel electrophoresis (Agilent 2100 Bioanalyzer; Agilent Technologies, Inc., Palo Alto, CA). Single-end cDNA libraries were prepared for each sample and sequenced using the Illumina TruSeq RNA Sample Preparation Kit by following the manufacture’s recommended procedures and sequenced using the Illumina HiSeq 2000. Library construction and RNA sequencing were performed in the Columbia Genome Center in Columbia University Medical Center.
Data processing and analysis
The short reads were mapped to the reference genome (Human: NCBI/build37.2) using TopHat (version 2.0.4)  with 4 mismatches (−−read-mismatches = 4) and 10 maximum multiple hits (−−max-multihits = 10). Transcripts were assembled and the relative abundance (aka expression level) of genes and splice isoforms were estimated using Cufflinks in “fragments per kilobase of exon model per million mapped reads” (FPKM) . (version 2.0.2) with default settings. Differential expression genes and exomes were tested using DEseq. To define significantly differential expression genes/exomes, we used a p-value < 0.05 as the cutoff. The Database for Annotation, Visualization and Integrated Discovery (DAVID), v6.7, (http://david.abcc.ncifcrf.gov/home.jsp) was used for Gene Ontology (GO) analysis.
Ingenuity Pathway Analysis (IPA)
To identify upstream regulators of the differentially expressed genes between AD and CRM or between AD and CF, we used IPA software (Ingenuity Systems, Redwood City, CA). Gene symbols were used as identifiers and the Ingenuity Knowledge Base gene set as a reference for a pathway analysis. Identification of upstream transcription regulator was assessed using IPA where the activation or inhibition of a transcription regulator was determined from expression patterns of the transcription factor and its downstream-regulated genes within the differentially expressed list. The absolute value of the z-score ≥ 2.0 was considered statistically significant with a positive value indicating activation and a negative values indicating inhibition of the transcription factor.
Differentially expressed genes and LncRNA expression validation by quantitative real-time RT-PCR
Primers used for real-time PCR and real-time PCR validation of RNA-seq results
Primers sequence (5′ ~ 3′)
Fold change (AD vs CRM)
GAA GCA GCA GGA AGC TGA A
GGA TGT CTC TCA GTT GCT CAA A
TTG GAT AAG TGC ATG GAG GAG
CCT GTT TGA CGA AGA ACA TTC AG
CCT GTT CAA CAC CCT CTT CA
CAT GAG GAT GCC CAG AAT CA
ACG AAC AGT CGA AAG GGA AC
AGC CAT CGA CAG ACT TGA TTT
GGG CTA TGA CCT GGA GTT AAG
CAC CTC ATA GTT CCT CCA CTT C
GAA GCC TGT CAA AGA GAG AGA G
GTT AGG TTT ATA GCC GCC AGT
ACA ACA TCT TCT GCT CCA ACC
TGT CGC TGC TGG ATC TCT
CTG GTT GTG TCA CCT CCT AAC
GTC CTT GTG TCC ATG CAT CT
GAT GGG ATG CCT ATT GCT ACA
CGC TGA CCA TAC TTG AGT CTA AT
GCA GGA AGC TGA AGA GTT AGA
GAA TGT CTT CCT CCT CCT TCT C
An overview of RNA-seq data
RNA-Seq sequences reads mapping to NCBI human genome build37.2 by TopHat (version.2.0.4)
Number of raw reads
Number of mapped reads
Mapped reads %
RNA-SeQC analysis of sequencing performance and library quality
Expression profiling efficiency
Gene expression analysis
Gene ontology analysis of genes expressed in neutrophils
High expression genes
Intracellular signaling cascade
Medium expression genes
Protein catabolic process
Cellular macromolecule catabolic process
Proteolysis involved in cellular protein catabolic process
Modification-dependent macromolecule catabolic process
Modification-dependent protein catabolic process
Low expression genes
Response to DNA damage stimulus
DNA metabolic process
Regulation of transcription
We used Cufflinks to assemble transcripts and estimate the abundance of isoforms. There are 31,046 isoforms expressed in neutrophils in at least one of nine subjects with FPKM value >0 (Additional file 4: Table S4). While the genes have several alternatively spliced transcripts, their isoforms are not expressed at equivalent levels. For most genes, one isoform is expressed more highly than others (data not shown), a finding compatible with what has been reported in B cells .
Identification of differentially expressed genes between phenotypes
Although neutrophils are typically regarded as non-specific mediators of inflammatory responses, data from hybridization-based gene expression arrays suggest that there may be subtle differences in neutrophil transcriptomes that correlate with human disease phenotypes . We used DESeq  to test the differential expression of genes comparing the three phenotypes. When the expressed genes are defined as FPKM ≥1 in all 3 replicates, there are 8597 expressed genes in the AD neutrophils, 8668 expressed genes in CRM neutrophils and 8102 expressed genes in the CF neutrophils, which were aligned to the reference genome (data no shown).
Top 10 up and down regulated differentially expressed genes in neutrophils in juvenile idiopathic arthritis with active status compared with in juvenile rheumatoid arthritis with clinical remission on medicine status
Fold change (AD vs CRM)
We further examined the specificity of neutrophil transcriptomes by examining another phenotype characterized by chronic, soft tissue inflammation. CF is an autosomal-recessively inherited disease caused by mutations in the CFTR gene that lead to abnormal ion transport in respiratory epithelial cells [34, 35]. These abnormalities are associated with chronic, indolent inflammation in the small airways and lung parenchyma, due, in part, to chronic infection/colonization with pseudomonas aerigninosa as well as other bacteria . Lungs of affected patients show a characteristic neutrophilic infiltrate and high concentrations of tumor necrosis factor-alpha (TNF-a), which amplifies the inflammatory process by stimulating release of IL-1, IL-6, IL-8 [37, 38]. CF thus presents a good control and comparison group to children with JIA, as it allows us to identify transcriptional reorganization that is disease-specific and distinguish it from changes that are generic to chronic inflammation in soft tissues.
Top 10 up and down regulated differentially expressed genes in neutrophils in juvenile idiopathic arthritis with active status compared with in neutrophils in cystic fibrosis
(AD vs CF)
To confirm the differences in gene expression between AD and CRM observed in the RNA-seq experiments, we performed real-time qRT-PCR. Ten genes that showed significant differentially expressed between AD and CRM in the RNA-seq analysis (DDX60, IFH1, IFITM3, IGHMBP, MOV10, OAS1, PML, RNF213, TNFAIP6, TRIM5) were analyzed by real-time qRT-PCR in an independent patient cohort. Table 1 shows that seven of ten genes differentially expressed in the RNAseq analysis were also differentially expressed in the real-time qRT-PCR.
Transcription factor networks
Differential usage of exons
We applied DEXSeq  to test for differential exon usage in RNA-seq data. The DEXSeq analysis revealed that there were 204 significantly differential exon usages (p < 0.01) between AD and CRM (Additional file 6: Table S6). These differential exon usages were distributed in 179 genes. Of 179 genes demonstrating differential exon usage, 7 genes (BTN3A2, IFI44L, IFIT3, LOXHD1, PAM, RNF213, SH3RF3) also showed differential expression between AD and CRM at the gene level.
We also found 838 genes that demonstrated significant differential exon usage (p < 0.01) when we compared AD and CF (Additional file 7: Table S7). Differential exon usage was distributed in 678 genes. Of 678 genes demonstrating differential exon usage, 6 genes (IFITM2, LOXHD1, MSH2,, RPH3A, SH3RF3, ZNF107) also showed differential expression between AD and CF at the gene level.
The comparison of AD vs. CRM reveals a neutrophil response to therapeutic intervention and thus may be somewhat artificial. In contrast, the CF and AD situations are rather similar (chronic inflammation in soft tissues), and subtle re-organization of the transcriptome in these parallel but slight different scenarios is actually quite interesting.
Differentially expressed lncRNA
As noted above, we identified 2981 lncRNAs expressed in neutrophils in at least one of nine subjects. We applied differential expression analysis of all the lncRNAs that were expressed in all the libraries using DESeq. The analysis revealed there are 38 lncRNAs that showed differential expression when the AD group was compared with CRM. There were 30 lncRNAs that showed detectable differences in expression between AD and CF. There were 63 DE lncRNAs in CRM vs CF comparison (Additional file 8: Table S8). These results show that lncRNA expression is disease and disease-state specific.
To validate the RNA-seq data, we chose randomly 7 of 38 DE lncRNAs between AD and CRM, and performed real-time PCR in an independent patient cohort. Table 1 shows that 7 lncRNAs differentially expressed in the RNA-seq analysis were also differentially expressed in real-time PCR. It is interesting to note that two of these lncRNA, lncIFITM-4 and lncPML-1, are adjacent to differentially-expressed genes (IFITM3 and PML, respectively), suggesting that these transcripts may act directly in to regulate gene expression in neutrophils and fine-tune transcriptional responses to specific inflammatory/disease states. As the functions of lncRNAs are largely unknown, an approach for inferring putative functions of long ncRNAs is to examine protein-coding genes located near ncRNAs of interest [46, 47]. We examined the expression pattern of paired neighbor protein-coding genes of differential expressed lncRNAs between AD and CRM. Interestingly, we found that six neighbor protein-coding genes (CHSY1, IFITM3, LILRA5, PGM5, PML, ZCCHC2) of these DE lncRNAs were also differentially expressed at same direction. Of these 5 genes, IFITM3 mRNA and PML mRNA are known to be upregulated in labial minor salivary glands and associated with in primary Sjogren’s syndrome .
We have demonstrated the feasibility of preparing high-quality RNA from human neutrophils in sufficient quantity to perform RNA-Seq in the context of different human phenotypes. Furthermore, we have demonstrated that such studies can be undertaken even with the relatively small amounts of human blood available for translational studies in children. Finally, we demonstrate that neutrophil transcriptomes show subtle variations that correspond to specific human phenotypes and inflammatory conditions.
Neutrophils are a critical cell in human defense, and depletion of neutrophils from peripheral blood, whether as a consequence of therapeutic efforts or human disease, has almost immediate adverse consequences . Furthermore, neutrophils play a critical role in their ability to direct and instruct the adaptive immune system . Despite the importance of these cells, they are conspicuously absent from the both ENCODE and Roadmap Epigenomics data sets. Thus, we know little of the functional genomics of these cells and how neutrophil genomes adapt to regulate transcription in response to external signals and disease states.
Like most leukocyte genomes, neutrophils show substantial transcriptional complexity. Although most transcripts were expressed at low levels (compared with lymphocytes, for example) we found that more than 7700 genes, or about 30 % of all the known protein-coding genes, were detected in all nine samples. Furthermore, isoform usage was extensive, with more than 9500 detected in each of the three different phenotypes. While extensive RNA splicing is known to characterize adaptive immune responses , these findings suggest that even neutrophils carry the capacity for supple, threat-specific adaptations to host injury or infection. The latter point is corroborated by the subtle differences in the transcriptomes of the three different childhood phenotypes that we studied.
Because of the differences between the three different phenotypes, our studies suggest that RNAseq may be a substantial improvement over hybridization-based gene expression arrays for the development of informative biomarkers in human disease. As gene expression microarrays became widely available and affordable, there was considerable excitement about their use in developing predictive or diagnostic biomarkers . It was disappointing, then, when biomarkers identified in one cohort (e.g., for prediction of response to therapy) showed little or overlap with biomarkers developed in independent cohorts [52, 53]. While there have been successful attempts to corroborate gene array data in independent patient cohorts , the limited dynamic range and considerable technical variation (large batch effects) of hybridization-based arrays will very likely continue to limit their utility for medical purposes. With next-gen sequencing costs continuing to fall, the possibility of developing “personalized transcriptomes” for diagnosis or prognosis (e.g., predicting therapeutic response) seems an achievable goal.
This sample size is too small to make broad inferences about the pathogenesis of JIA, although some interesting findings emerge from these data. For example, we have previously reported the involvement of interferon gamma in gene expression networks constructed from gene microarray data in JIA neutrophils . The current data suggest an attenuation of type 1 interferon responses in JIA neutrophils, a new finding and not one that we have previously discerned in expression data in JIA neutrophils [1, 3, 26], although we see decreased expression of numerous pro-inflammatory genes in JIA neutrophils using this approach. These findings are probably medically relevant: a recent study shows that IFN-response gene expression levels in neutrophils in adult RA correlates with a good response to TNF inhibitor therapy . While we believe that our findings support efforts to continue biomarker development from human neutrophils in chronic inflammatory diseases, it is unlikely that, by itself, neutrophil transcriptome profiling will not be sufficient to crisply elucidate the pathogenesis of JIA or other complex traits characterized by chronic, indolent inflammation.
RNAseq also allowed us to characterized the expression of lncRNAs in neutrophils in CF patients and JIA patients with different disease states. Our results demonstrate a large number of lncRNAs commonly expressed in neutrophils, and these data thus provide a useful resource for lncRNA expression in human neutrophils. We also analyzed differential expression analysis of the lncRNAs between diseases or disease states and identified 38 differentially expressed lncRNAs in AD vs CRM comparison, and 30 in AD vs CF. These results provide further evidence that neutrophils exhibit considerable adaptability in their transciptomes and that gene and transcript expression is disease or disease state specific.
Human neutrophils exhibit surprising specificity in their transcriptional responses, which vary both between specific diseases and even with specific disease states. These findings were observed for genes, gene isoforms, and non-coding transcripts. Furthermore, our findings show that RNA sequencing may be a useful method for investigating the connections between gene/transcript expression and human phenotypes, including disease phenotypes.
Support was received from the National Institutes of Health (R01-AR-060604) and an Innovative Research Grant from the Arthritis Foundation (JNJ).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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