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. 2016 Mar 7;5:291. [Version 1] doi: 10.12688/f1000research.8182.1

A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery

Darawan Rinchai 1,a, Sabri Boughorbel 2, Scott Presnell 3, Charlie Quinn 3, Damien Chaussabel 1
PMCID: PMC4856112  PMID: 27158452

Abstract

Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online at http://monocyte.gxbsidra.org/dm3/landing.gsp.

Keywords: Monocyte, Transcriptomics, Gene Expression Browser, Immunology, Bioinformatics

Introduction

Platforms such as microarrays and, more recently, next generation sequencing have been leveraged to generate molecular profiles at the scale of entire systems. The global perspective gained using such approaches is potentially transformative. Transcriptome profiling enabled for instance the characterization of molecular perturbations that occur in the context of a wide range disease processes 110. This in turn has provided opportunities for the discovery of biomarkers and for the development of novel therapeutic modalities 3, 1113. More recently such systems-scale profiling of the blood transcriptome has also been used to monitor response to vaccines or therapeutic drugs 1419. The democratization of these approaches has led to proliferation of data in public repositories: over 1.7 million individual transcriptome profiles from more than 65,000 studies have been deposited to date in the NCBI Gene Expression Omnibus (GEO), a public repository of transcriptome profiles.

Taken together this vast body of “collective data” holds the promise of accelerating the pace of biomedical discovery by creating countless opportunities for identifying and filling critical knowledge gaps. Building tools that provide biomedical researchers with the ability to seamlessly interact with collections of datasets along with rich contextual information is essential in promoting insight and enabling knowledge discovery. To address this need we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB).

GXB was described in a recent publication and is available as open source software on GitHub 20. This tool constitutes a simple interface for the browsing and interactive visualization of large volumes of heterogeneous data. Users can easily customize data plots by adding multiple layers of information, modifying the order of samples, and generating links that capture these settings which can be inserted in email communications or in publications. Accessing the tool via these links also provides access to rich contextual information that is essential for data interpretation. This includes access to gene information and relevant literature, study design information, detailed sample information as well as ancillary data 20.

In recent years, a large number of transcriptional studies have been conducted aiming at the characterization and functional classification of monocytes in health and disease. Monocytes are a population of immune cells found in the blood, bone marrow, and spleen. They constitute ~10% of the total circulating blood leukocytes in humans. They can remain in the blood circulation for up to 1–2 days, after which time, if they have not been recruited to a tissue, they die and are removed. They are considered the systemic reservoir of myeloid precursors for renewal of tissue macrophages and dendritic cells. Monocytes play a key role during immune response as professional phagocytes 21, 22, and producers of immune mediators 23, 24. Indeed, reports show that monocytes are recruited at the site of infections as innate effectors of the inflammatory response to microbes, killing pathogens via phagocytosis, production of reactive oxygen intermediate (ROIs) 25, reactive nitrogen intermediate (RNIs) 26, 27, myeloperoxidase (MPO) 28, 29, and producing inflammatory cytokines 30 that contribute to further amplifying the antimicrobial response 31.

Human monocytes are derived from hematopoietic stem cells in the bone marrow and are released into peripheral blood circulation upon maturation. They are divided into three major subsets based on the expression of cell surface markers CD14 and CD16. The most prevalent subset in the blood circulation, accounting for 90% of all monocytes, are the classical monocytes that express high levels of CD14 but low levels of CD16. The remaining 10% is divided into two subsets: intermediate monocyte with high expression of CD14 and CD16 (CD14+CD16+) and non-classical monocytes that express low levels of CD14 but high levels of CD16 (CD14dimCD16++ or CD14+CD16++) 3234.

In this data note we are making available via GXB a curated compendium of 93 public datasets relevant to human monocyte immunobiology, representing a total of 4,516 transcriptome profiles.

Materials and methods

Identification of monocyte datasets

Potentially relevant datasets deposited in GEO were identified using an advanced query based on the Bioconductor package GEOmetadb and the SQLite database that captures detailed information on the GEO data structure; https://www.bioconductor.org/packages/release/bioc/html/GEOmetadb.html 35. The search query was designed to retrieve entries where the title and description contained the word Monocyte OR Monocytes, were generated from human samples, using Illumina or Affymetrix commercial platforms. The query result is appended with rich metadata from GEOmetadb that allows for manual filtering of the retrieved collection.

The relevance of each entry returned by this query was assessed individually. This process involved reading through the descriptions and examining the list of available samples and their annotations. Sometimes it was also necessary to review the original published report in which the design of the study and generation of the dataset is described in more detail. The datasets cover a broad range of human immunology studies investigating monocyte immunobiology in the context of diseases and through comparison with diverse cell populations and study types as illustrated by a graphical representation of relative occurrences of terms in the list of diseases loaded into our tool ( Figure 1). A wide range of cell types and diseases are represented. Ultimately, the collection was comprised of 93 curated datasets. It includes datasets generated from studies profiling primary human CD14+ cells isolated from patients with autoimmune diseases (7), bacterial, virus and parasite infections (7), cancer (4), cardiovascular diseases (4), kidney diseases (4), as well as monocytes isolated from healthy subjects (58) ( Figure 2). The 58 datasets in which monocytes were isolated from healthy subjects were classified based on whether profiling was conducted ex vivo or following in vitro experiments. In total 38 datasets were identified in which primary human CD14+ cells were stimulated or infected in in vitro experiments ( Figure 2). Among the many noteworthy datasets, there are 8 datasets investigating differences between monocytes subsets; classical (CD14++CD16-), intermediate (CD14+CD16+) and non-classical monocytes (CD14-CD16++) 3234 [GXB: GSE16836, GSE18565, GSE25913, GSE34515, GSE35457, GSE51997, GSE60601, GSE66936]. Another dataset from Banchereau and colleagues investigated responses of monocyte and dendritic cells to 13 different vaccines in vitro 36 [GXB: GSE44721]. The datasets that comprise our collection are listed in Table 1 and can be browsed interactively in GXB.

Figure 1. Thematic composition of the dataset collection.

Figure 1.

Word frequencies extracted from text descriptions of the studies loaded into the GXB tool are depicted as a word cloud. The size of the words is proportional to their frequency.

Figure 2. Break down of the dataset collection by category.

Figure 2.

The pie chart on the left panel indicates dataset frequencies by disease status. The chart on the right panel indicates the type of studies carried out for the 58 datasets consisting of monocyte obtained exclusively from healthy donors.

Table 1. List of datasets constituting the collection.

Title Platforms Diseases Number of
sample
Experiments GEO ID References
Interaction of bone marrow stroma and monocytes: bone marrow
stromal cell lines cultured with monocytes
Affymetrix Healthy 8 In vitro GSE10595 37
Monocyte gene expression profiling in familial combined
hyperlipidemia and its modification by atorvastatin treatment
Affymetrix Familial combined
hyperlipidemia
9 In vitro GSE11393 38
Performance comparison of Affymetrix and Illumina microarray
technologies
Affymetrix Acute coronary syndrome 10 Ex vivo GSE11430 39
Gene expression profiling in pediatric meningococcal sepsis
reveals dynamic changes in NK-cell and cytotoxic molecules
Affymetrix Meningococcal sepsis 41 Ex vivo GSE11755 N/A
Effect of interferon-gamma on macrophage differentiation and
response to Toll-like receptor ligands
Affymetrix Healthy 10 In vitro GSE11864 40
Human monocyte and dendritic Cell Subtype Gene Arrays Affymetrix Healthy 8 Ex vivo GSE11943 41
Microarray analysis of human monocytes infected with Francisella
tularensis
Affymetrix Healthy 14 In vitro GSE12108 42
Human blood monocyte profile in Ventilator-Associated Pneumonia
patients
Affymetrix Pneumonia 60 Ex vivo GSE12838 N/A
Quercetin supplementation and CD14+ monocyte gene expression Affymetrix Healthy 6 Ex vivo GSE13899 43
Effects of PMN-Ectosomes on human macrophages Affymetrix Healthy 16 In vitro GSE14419 N/A
Homogeneous monocytes and macrophages from hES cells
following coculture-free differentiation in M-CSF and IL-3
Affymetrix Healthy 9 Ex vivo GSE15791 44
Expression data from human macrophages Affymetrix Healthy 38 In vitro GSE16385 45
Transcriptional profiling of CD16+ and CD16- peripheral blood
monocytes from healthy individuals
Affymetrix Healthy 8 Ex vivo GSE16836 32
COPD-Specific Gene Expression Signatures of Alveolar
Macrophages as well as Peripheral Blood Monocytes Overlap and
Correlate with Lung Function
Affymetrix Chronic obstructive
pulmonary disease
12 Ex vivo GSE16972 46
Loss-of-function mutations in REP-1 affect intracellular vesicle
transport in fibroblasts and monocytes of CHM patients
Affymetrix Choroideremia 15 Ex vivo GSE17549 47
Effect of two weeks erythropoietin treatment on monocyte
transcriptomes of cardiorenal patients
Illumina Cardiorenal syndrome 48 Ex vivo GSE17582 N/A
Comparison of gene expression profiles between human
monocyte subsets
Affymetrix Healthy 6 Ex vivo GSE18565 48
Subpopulations of CD163 positive macrophages are classically
activated in psoriasis
Illumina Psoriasis 58 Ex vivo GSE18686 49
Mycobacterium tuberculosis Chaperonin 60.1 has Bipolar Effects
on Human peripheral blood-derived Monocytes
Affymetrix Healthy 21 In vitro GSE18794 N/A
Blood Transcriptional Profiles of Active TB (Separated cell) Illumina Tuberculosis 44 Ex vivo GSE19443 11
Filaria induced monocyte dysfunction and its reversal following
treatment
Affymetrix Filariasis 14 Ex vivo GSE2135 50
Ubiquinol-induced gene expression signatures are translated into
reduced erythropoiesis and LDL cholesterol levels in humans
Affymetrix Healthy 6 Ex vivo GSE21351 51
Monocyte vs Macrophage Study Affymetrix Healthy 6 In vitro GSE22373 52
Monocyte gene expression patterns distinguish subjects with and
without atherosclerosis
Illumina Carotid atherosclerosis 95 Ex vivo GSE23746 N/A
Deconvoluting Early Post-Transplant Immunity Using Purified Cell
Subsets Reveals Functional Networks Not Evident by Whole Blood
Analysis
Affymetrix Kidney transplantation 179 Ex vivo GSE24223 53
Cooperative and redundant signaling of leukotriene B4 and
leukotriene D4 in human monocytes
Affymetrix Healthy 10 In vitro GSE24869 54
Gene expression profiling of the classical (CD14++CD16-),
intermediate (CD14++CD16+) and nonclassical (CD14+CD16+)
human monocyte subsets
Illumina Healthy 24 Ex vivo GSE25913 34
Direct Cell Conversion of Human Fibroblasts to Monocytic
phagocytes by Forced Expression of Monocytic Regulatory
Network Elements
Illumina Dermatomyositis 15 Ex vivo GSE27304 N/A
cMyb and vMyb in human monocytes Affymetrix Healthy 6 In vitro GSE2816 55
Temporal transcriptional changes in human monocytes following
acute myocardial infarction: The GerMIFs monocyte expression study
Illumina Acute myocardial
infarction
76 Ex vivo GSE28454 N/A
mRNA expression profiling of human immune cell subset (Roche) Affymetrix Healthy 47 Ex vivo GSE28490 56
mRNA expression profiling of human immune cell subsets (HUG) Affymetrix Healthy 33 Ex vivo GSE28491 56
Changes in gene expression profiles in patients with 5q- syndrome
in CD14+ monocytes caused by lenalidomide treatment
Illumina 5q- syndrome 17 Ex vivo GSE31460 N/A
Genome-wide analysis of lupus immune complex stimulation of
purified CD14+ monocytes and how this response is regulated by C1q
Illumina Healthy 8 In vitro GSE32278 57
Transcriptome analysis of circulating monocytes in obese patients
before and three months after bariatric surgery
Illumina Obesity 48 Ex vivo GSE32575 58
CD4 on human monocytes Affymetrix Healthy 6 In vitro GSE32939 59
Peripheral Blood Monocyte Gene Expression in Recent-Onset
Type 1 Diabetes
Illumina Type 1 Diabetes 22 Ex vivo GSE33440 60
Traffic-related Particulate Matter Upregulates Allergic Responses
by a Notch-pathway Dependent Mechanism
Affymetrix Healthy 16 In vitro GSE34025 N/A
Human monocyte activation with NOD2L vs. TLR2/1L Affymetrix Healthy 45 In vitro GSE34156 61
Bacillus anthracis' lethal toxin induces broad transcriptional
responses in human peripheral monocyte
Affymetrix Healthy 8 In vitro GSE34407 62
Gene expression profiles of human blood classical monocytes
(CD14++CD16-), CD16 positive monocytes (CD14+16++ and
CD14++CD16+), and CD1c+ CD19- dendritic cells
Affymetrix Healthy 9 Ex vivo GSE34515 N/A
Genome-wide analysis of monocytes and T cells' response to
interferon beta
Illumina Healthy 12 In vitro GSE34627 63
Highly pathogenic influenza virus inhibit Inflammatory Responses
in Monocytes via Activation of the Rar-Related Orphan Receptor
Alpha (RORalpa)
Affymetrix Healthy 12 In vitro GSE35283 N/A
Transcriptome profiles of human monocyte and dendritic cell subsets Illumina Healthy 49 Ex vivo GSE35457 64
Influenza virus A infected monocytes Illumina Healthy 6 In vitro GSE35473 65
PGE2-induced OSM expression Affymetrix Chronic wound 6 Ex vivo GSE36995 66
Inflammatory Expression Profiles in Monocyte to Macrophage
Differentiation amongst Patients with Systemic Lupus
Erythematosus and Healthy Controls with and without an
Atherosclerosis Phenotype
Illumina Systemic lupus
erythematosus
72 Ex vivo GSE37356 N/A
New insights into key genes and pathways involved in the
pathogenesis of HLA-B27-associated acute anterior uveitis
Affymetrix Acute anterior uveitis 6 In vitro GSE37588 N/A
Analysis of blood myelomonocytic cells from RCC patients Illumina Renal cell carcinoma 8 Ex vivo GSE38424 67
Nanotoxicogenomic study of ZnO and TiO2 responses Illumina Healthy 90 In vitro GSE39316 N/A
Macrophage Microvesicles Induce Macrophage Differentiation
and miR-223 Transfer
Affymetrix Healthy 24 In vitro GSE41889 68
TREM-1 is a novel therapeutic target in Psoriasis Affymetrix Psoriasis 15 In vitro GSE42305 69
Comparison study between Uremic patient with Healthy control Affymetrix Chronic kidney disease 6 Ex vivo GSE43484 N/A
Microarray analysis of IL-10 stimulated adherent peripheral blood
mononuclear cells
Affymetrix Healthy 8 In vitro GSE43700 70
Monocytes and Dendritic cells stimulated by 13 human vaccines
and LPS
Illumina Vaccination 128 In vitro GSE44721 36
Gene expression profile of human monocytes stimulated with
all-trans retinoic acid (ATRA) or 1,25a-dihydroxyvitamin D3 (1,25D3)
Affymetrix Healthy 12 In vitro GSE46268 71
Transcriptome analysis of blood monocytes from sepsis patients Illumina Sepsis 44 Ex vivo GSE46955 72
Tumor-educated circulating monocytes are powerful specific
biomarkers for diagnosis of colorectal cancer
Illumina Colorectal cancer 93 Ex vivo GSE47756 73
Similarities and differences between macrophage polarized gene
profiles
Illumina Healthy 12 In vitro GSE49240 74
The effect of cell subset isolation method on gene expression in
leukocytes.
Illumina Healthy 50 Ex vivo GSE50008 N/A
Transcriptome analysis of HIV-infected peripheral blood
monocytes
Illumina HIV 86 Ex vivo GSE50011 75
Gene expression profiles in T-lymphocytes and Monocytes of
participants of the Tour de France 2005
Affymetrix Healthy 66 Ex vivo GSE5105 N/A
Effects of exercise on gene expression level in human monocytes Affymetrix Healthy 24 Ex vivo GSE51835 76
T helper lymphocyte- and monocyte-specific type I interferon (IFN)
signatures in autoimmunity and viral infection.
Affymetrix Autoimmune diseases 36 Ex vivo GSE51997 77
Longitudinal comparison of monocytes from an HIV viremic vs
avirmeic state
Affymetrix HIV 16 Ex vivo GSE5220 78
Expression data from monocytes and monocyte derived
macrophages
Affymetrix Healthy 12 In vitro GSE52647 N/A
Transcriptome analysis of primary monocytes from HIV+ patients
with differential responses to therapy
Illumina HIV 14 Ex vivo GSE52900 79
Human blood monocyte response to IL-17A in culture Affymetrix Healthy 6 In vitro GSE54884 N/A
Divergent genome wide transcriptional profiles from immune
cell subsets isolated from SLE patients with different ancestral
backgrounds
Illumina Systemic lupus
erythematosus
208 Ex vivo GSE55447 80
Cell Specific Expression & Pathway Analyses Reveal Novel
Alterations in Trauma-Related Human T-Cell & Monocyte Pathways
Affymetrix Trauma patients 42 Ex vivo GSE5580 81
Immune Variation Project (ImmVar) [CD14] Affymetrix Healthy 485 Ex vivo GSE56034 N/A
Transcriptomics of human monocytes Illumina Healthy 1202 Ex vivo GSE56045 82
Effect of vitamin D treatment on human monocyte Affymetrix Healthy 16 In vitro GSE56490 N/A
Monocytes of patients with familial hypercholesterolemia show
alterations in cholesterol metabolism
Affymetrix Hypercholesterolemia 23 Ex vivo GSE6054 83
Gene expression data from CD14++ CD16- classical monocytes
from healthy volunteers and patients with pancreatic ductal
adenocarcinoma
Affymetrix Pancreatic ductal
adenocarcinoma
12 Ex vivo GSE60601 N/A
Activation of the JAK/STAT pathway in Behcet's Disease Affymetrix Behcet’s disease 29 Ex vivo GSE61399 N/A
Alarmins MRP8 and MRP14 induce stress-tolerance in phagocytes
under sterile inflammatory conditions
Illumina Sterile inflammation 12 In vitro GSE61477 N/A
GM-CSF induced gene-regulation in human monocytes Affymetrix Healthy 6 In vitro GSE63662 84
Treatment of human monocytes with TLR7 or TLR8 agonists Affymetrix Healthy 9 In vitro GSE64480 85
Restricted Dendritic Cell and Monocyte Progenitors in Human
Cord Blood and Bone Marrow
Illumina Healthy 36 Ex vivo GSE65128 86
Interleukin-1- and Type I Interferon-Dependent Enhanced
Immunogenicity of an NYVAC-HIV-1 Env-Gag-Pol-Nef Vaccine
Vector with Dual Deletions of Type I and Type II Interferon-Binding
Proteins
Illumina Vaccination 20 In vitro GSE65412 N/A
Comparative analysis of monocytes from healthy donors, patients
with metastatic breast cancer, sepsis or tuberculosis.
Illumina Breast cancer and
Bacterial infection
13 Ex vivo GSE65517 87
Expression data from intermediate monocytes from healthy donors
and autoimmune uveitis patients
Affymetrix Autoimmune uveitis 21 Ex vivo GSE66936 88
Induction of Dendritic Cell-like Phenotype in Macrophages during
Foam Cell Formation
Affymetrix Healthy 22 In vitro GSE7138 89
Genome Wide Gene Expression Study of Circulating Monocytes in
human with extremely high vs. low bone mass
Affymetrix Healthy 26 Ex vivo GSE7158 N/A
Genomic profiles for human peripheral blood T cells, B cells,
natural killer cells, monocytes, and polymorphonuclear cells:
comparisons to ischemic stroke, migraine, and Tourette syndrome
Affymetrix Healthy 18 Ex vivo GSE72642 90
Expression data from monocytes of individuals with different
collateral flow index CFI
Affymetrix Coronary artery disease 160 Ex vivo GSE7638 91
Leukotriene D4 induces gene expression in human monocytes
through cysteinyl leukotriene type I receptor
Affymetrix Healthy 8 In vitro GSE7807 92
Gene expression profile during monocytes to macrophage
differentiation
Affymetrix Healthy 9 In vitro GSE8286 N/A
Toll-like receptor triggering of a vitamin D-mediated human
antimicrobial response
Affymetrix Healthy 50 In vitro GSE8921 93
TRAIL Is a Novel Antiviral Protein against Dengue Virus Affymetrix Dengue 10 In vitro GSE9378 N/A
Gene Expression-Based High Throughput Screening: APL
Treatment with Candidate Compounds
Affymetrix Leukemia 24 Ex vivo GSE976 94
Innate immune responses to TREM-1 activation Affymetrix Healthy 11 In vitro GSE9988 95

Dataset upload and annotation on GXB

Once a final selection had been made each dataset was downloaded from GEO in the SOFT file format. It was in turn uploaded on an instance of the Gene Expression Browser (GXB) hosted on the Amazon Web Services cloud. Available sample and study information were also uploaded. Samples were grouped according to possible interpretations of study results and ranking based on the different group comparisons that were computed (e.g. comparing monocyte isolated from case vs controls in studies where profiling was performed ex-vivo; or stimulated vs medium control in in vitro experiments).

Short Gene Expression Brower tutorial

The GXB software has been described in detail in a recent publication 20. This custom software interface provides users with a means to easily navigate and filter the dataset collection available at http://monocyte.gxbsidra.org/dm3/landing.gsp. A web tutorial is also available online: http://monocyte.gxbsidra.org/dm3/tutorials.gsp#gxbtut. Briefly, datasets of interest can be quickly identified either by filtering using criteria from pre-defined lists on the left or by entering a query term in the search box at the top of the dataset navigation page. Clicking on one of the studies listed in the dataset navigation page opens a viewer designed to provide interactive browsing and graphic representations of large-scale data in an interpretable format. This interface is designed to present ranked gene lists and display expression results graphically in a context-rich environment. Selecting a gene from the rank ordered list on the left of the data-viewing interface will display its expression values graphically in the screen’s central panel. Directly above the graphical display drop down menus give users the ability: a) To change how the gene list is ranked; this allows the user to change the method used to rank the genes, or to include only genes that are selected for specific biological interest; b) To change sample grouping (Group Set button), in some datasets a user can switch between groups based on cell type to groups based on disease type, for example; c) To sort individual samples within a group based on associated categorical or continuous variables (e.g. gender or age); d) To toggle between the bar chart view and a box plot view, with expression values represented as a single point for each sample. Samples are split into the same groups whether displayed as a bar chart or box plot; e) To provide a color legend for the sample groups; f) To select categorical information that is to be overlaid at the bottom of the graph. For example, the user can display gender or smoking status in this manner; g) To provide a color legend for the categorical information overlaid at the bottom of the graph; and h) To download the graph as a png image or csv file for performing a separate analysis. Measurements have no intrinsic utility in absence of contextual information. It is this contextual information that makes the results of a study or experiment interpretable. It is therefore important to capture, integrate and display information that will give users the ability to interpret data and gain new insights from it. We have organized this information under different tabs directly above the graphical display. The tabs can be hidden to make more room for displaying the data plots, or revealed by clicking on the blue “show info panel” button on the top right corner of the display. Information about the gene selected from the list on the left side of the display is available under the “Gene” tab. Information about the study is available under the “Study” tab. Information available about individual samples is provided under the “Sample” tab. Rolling the mouse cursor over a bar chart's element while displaying the “Sample” tab lists any clinical, demographic, or laboratory information available for the selected sample. Finally, the “Downloads” tab allows advanced users to retrieve the original dataset for analysis outside this tool. It also provides all available sample annotation data for use alongside the expression data in third party analysis software. Other functionalities are provided under the “Tools” drop-down menu located in the top right corner of the user interface. Some of the notable functionalities available through this menu include: a) Annotations, which provides access to all the ancillary information about the study, samples and dataset organized across different tabs; b) Cross-project view, which provides the ability for a given gene to browse through all available studies; c) Copy link, which generates a mini-URL encapsulating information about the display settings in use and that can be saved and shared with others (clicking on the envelope icon on the toolbar inserts the url in an email message via the local email client); and d) Chart options, which gives user the option to customize chart labels.

Dataset validation

Quality control checks were performed with the examination of profiles of relevant biological indicators. Known leukocyte markers were used, such as CD14, which is expressed by monocytes and macrophages; as well as markers that would indicate significant contamination of the sample by other leukocyte populations: such as CD3, a T-cells marker; CD19, a B-cell marker; CD56, an NK cell marker ( Figure 3; The expression of the CD14 marker across all studies can be checked using the cross project functionality of GXB: http://monocyte.gxbsidra.org/dm3/geneBrowser/crossProject?probeID=201743_at&geneSymbol=CD14&geneID=929). In addition, expression of the XIST transcripts, in which expression is gender-specific, was also examined to determine its concordance with demographic information provided with the GEO submission.

Figure 3. Illustrative example showing the abundance levels of CD14 transcripts across samples in a given study.

Figure 3.

The expression of this gene is indicative of the purity of primary human monocyte preparation; this marker is expected to be high in monocyte preparations and low in other leukocyte populations. In this view of the GXB expression of CD14 can be visualized across projects listed on the left.

Data availability

The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2016 Rinchai D et al.

All datasets included in our curated collection are also available publically via the NCBI GEO website: http://www.ncbi.nlm.nih.gov/geo/; and are referenced throughout the manuscript by their GEO accession numbers (e.g. GSE25913). Signal files and sample description files can also be downloaded from the GXB tool under the “downloads” tab.

Acknowledgement

The authors would like to acknowledge all the investigators who decided to make their datasets publically available by sharing them in GEO.

Funding Statement

DR, SB and DC received support from the Qatar Foundation.

I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[version 1; referees: 1 approved

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F1000Res. 2016 Mar 21. doi: 10.5256/f1000research.8800.r12769

Referee response for version 1

Ping Chen 1, David Kuo 2,3

General Comments

Modern genomics, especially with the emergence of high-throughput next-generation sequencing, is generating data at such a rapid rate that new tools for organizing, visualizing, sharing, and integrating heterogeneous data in the context of scientific information are needed for scientists to efficiently use these published data. The Chaussabel group has recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB), to address this problem.

In this data note, Dr. Rinchai et al. report a compendium of ninety-six curated human monocyte transcriptome datasets from GEO spanning a broad range of diseases, cell types, and experiments. These datasets were then uploaded to the Gene Expression Browser for exploratory data analysis and dataset validation. The Gene Expression Browser should prove very useful for investigating large datasets; however, I have several questions and comments regarding the curated data itself:

Title:

The novel aspect and apparent emphasis of this data note is using the Gene Expression Browser to more easily explore the curated ninety-six datasets. But the current title emphasizes the key information on fostering the knowledge discovery. Please consider rephrasing it by focusing on the monocyte datasets and web application.

Introduction:

As the Gene Expression Browser has been described in detail previously, the emphasis of this data note should be on the curated data. It would be helpful to discuss the motivation for creating this particular compendium of monocyte transcriptome datasets as well as the intended use of the curated data given the breadth and heterogeneity of diseases, cell types, and experiments that it includes.

Methods:

1. Please elaborate more specifically on how the datasets were curated. What were the eligibility criteria for inclusion into the compendium?

2. The table summarizing the published data can difficult to read due to its landscape orientation. Consider rotating the table from a landscape orientation to a portrait orientation.

3. In the right pie chart of Figure 2, there are twelve datasets studying primary monocytes; however, datasets classified as in vitro stimulation, infection, and monocyte subsets may also contain primary monocytes. Better categorization is needed.

4. Data validation is critical for verifying that a dataset is acceptable for use.  The authors mention performing dataset validation but do not report the related results or summary of their validation.  On page 9, the process of assessing contamination by other leukocyte populations using surface markers should be done carefully as CD14 + monocytes do share surface marker CD4.

5. In Fig. 3, it is unclear whether the orange bar plot is referring to CD4 + T cells or CD4 + cells in general. They are different cell types.

We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

F1000Res. 2016 Mar 29.
Darawan Rinchai 1

We thank the reviewers for their valuable feedback and suggestions to improve our manuscript.

Title: 

Following the suggestion of the reviewers we changed the title of the manuscript to “A curated compendium of of transcriptome datasets of relevance to human monocyte immunobiology research”.

Introduction: 

Thanks for raising this point. We added a long paragraph and new references in the introduction to emphasize the role of monocyte across different diseases and the motivation for creating this compendium of monocyte transcriptome datasets.

Methods:

1. We have added information about how datasest were selected for inclusion in the collections in the methods section under the title “Identification of monocyte datasets”… Using the search query, the results also returned a number of datasets that did not include profiles of monocytes but instead of “monocyte-derived dendritic cells” or “monocyte-derived macrophages”. During our manual screen these were excluded as were studies employing monocytic cell lines. Only studies including primary human monocyte profiles were retained.”…

2. We agree with the reviewer that presenting the table using landscape orientation makes it difficult to read. We therefore changed table format from landscape to portrait orientation.

3. Thank you for pointing this out. We changed the label on this figure to read “ex-vivo, no treatment”. These include studies where monocytes were isolated from healthy subjects for comparison with other cell types, or evaluation of variation among healthy individuals.

4. Assessing contamination can indeed be difficult, especially using this type of data where cell-level information is lacking. We plan to explore with our bioinformatics collaborators the development of a "scoring" approach to better quantify potential contamination but this is not a simple matter to address. At this point we have simply verified for each dataset that expression of markers was consistent with grouping labels provided by depositors. We have added language in the manuscript to clarify this point.

5. Thank you for pointing out this typo on this label. This dataset focuses on genomic profile of human blood both CD4+ and CD8+ T cells, B cells, NK cells monocytes and neutrophil. Figure 3 was corrected accordingly as shown in the new Figure 3. 

F1000Res. 2016 Mar 16. doi: 10.5256/f1000research.8800.r12768

Referee response for version 1

Marc Pellegrini 1

In this short descriptive report the authors put their published Gene Expression Browser tool to work in arranging several thousand transcriptome profiles obtained from public datasets that looked at monocyte immunology. They were able to compare groups of monocytes based on phenotypic attributes and rank gene expression. The authors provide a nice summary of the technique and validation.

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Availability Statement

    The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2016 Rinchai D et al.

    All datasets included in our curated collection are also available publically via the NCBI GEO website: http://www.ncbi.nlm.nih.gov/geo/; and are referenced throughout the manuscript by their GEO accession numbers (e.g. GSE25913). Signal files and sample description files can also be downloaded from the GXB tool under the “downloads” tab.


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