ALL Metrics
-
Views
-
Downloads
Get PDF
Get XML
Cite
Export
Track
Data Note

A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery

[version 1; peer review: 1 approved, 1 approved with reservations]
PUBLISHED 07 Mar 2016
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS

This article is included in the Data: Use and Reuse collection.

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 processes110. This in turn has provided opportunities for the discovery of biomarkers and for the development of novel therapeutic modalities3,1113. More recently such systems-scale profiling of the blood transcriptome has also been used to monitor response to vaccines or therapeutic drugs1419. 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 GitHub20. 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 data20.

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 phagocytes21,22, and producers of immune mediators23,24. 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 cytokines30 that contribute to further amplifying the antimicrobial response31.

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.html35. 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 vitro36 [GXB: GSE44721]. The datasets that comprise our collection are listed in Table 1 and can be browsed interactively in GXB.

d550a47f-1497-46bc-a120-a60be2828d73_figure1.gif

Figure 1. Thematic composition of the dataset collection.

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.

d550a47f-1497-46bc-a120-a60be2828d73_figure2.gif

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

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.

TitlePlatformsDiseasesNumber of
sample
ExperimentsGEO IDReferences
Interaction of bone marrow stroma and monocytes: bone marrow
stromal cell lines cultured with monocytes
AffymetrixHealthy8In vitroGSE1059537
Monocyte gene expression profiling in familial combined
hyperlipidemia and its modification by atorvastatin treatment
AffymetrixFamilial combined
hyperlipidemia
9In vitroGSE1139338
Performance comparison of Affymetrix and Illumina microarray
technologies
AffymetrixAcute coronary syndrome10Ex vivoGSE1143039
Gene expression profiling in pediatric meningococcal sepsis
reveals dynamic changes in NK-cell and cytotoxic molecules
AffymetrixMeningococcal sepsis41Ex vivoGSE11755N/A
Effect of interferon-gamma on macrophage differentiation and
response to Toll-like receptor ligands
AffymetrixHealthy10In vitroGSE1186440
Human monocyte and dendritic Cell Subtype Gene ArraysAffymetrixHealthy8Ex vivoGSE1194341
Microarray analysis of human monocytes infected with Francisella
tularensis
AffymetrixHealthy14In vitroGSE1210842
Human blood monocyte profile in Ventilator-Associated Pneumonia
patients
AffymetrixPneumonia60Ex vivoGSE12838N/A
Quercetin supplementation and CD14+ monocyte gene expressionAffymetrixHealthy6Ex vivoGSE1389943
Effects of PMN-Ectosomes on human macrophagesAffymetrixHealthy16In vitroGSE14419N/A
Homogeneous monocytes and macrophages from hES cells
following coculture-free differentiation in M-CSF and IL-3
AffymetrixHealthy9Ex vivoGSE1579144
Expression data from human macrophagesAffymetrixHealthy38In vitroGSE1638545
Transcriptional profiling of CD16+ and CD16- peripheral blood
monocytes from healthy individuals
AffymetrixHealthy8Ex vivoGSE1683632
COPD-Specific Gene Expression Signatures of Alveolar
Macrophages as well as Peripheral Blood Monocytes Overlap and
Correlate with Lung Function
AffymetrixChronic obstructive
pulmonary disease
12Ex vivoGSE1697246
Loss-of-function mutations in REP-1 affect intracellular vesicle
transport in fibroblasts and monocytes of CHM patients
AffymetrixChoroideremia15Ex vivoGSE1754947
Effect of two weeks erythropoietin treatment on monocyte
transcriptomes of cardiorenal patients
IlluminaCardiorenal syndrome48Ex vivoGSE17582N/A
Comparison of gene expression profiles between human
monocyte subsets
AffymetrixHealthy6Ex vivoGSE1856548
Subpopulations of CD163 positive macrophages are classically
activated in psoriasis
Illumina Psoriasis58Ex vivoGSE1868649
Mycobacterium tuberculosis Chaperonin 60.1 has Bipolar Effects
on Human peripheral blood-derived Monocytes
AffymetrixHealthy21In vitroGSE18794N/A
Blood Transcriptional Profiles of Active TB (Separated cell)IlluminaTuberculosis44Ex vivoGSE1944311
Filaria induced monocyte dysfunction and its reversal following
treatment
AffymetrixFilariasis14Ex vivoGSE213550
Ubiquinol-induced gene expression signatures are translated into
reduced erythropoiesis and LDL cholesterol levels in humans
AffymetrixHealthy6Ex vivoGSE2135151
Monocyte vs Macrophage StudyAffymetrixHealthy6In vitroGSE2237352
Monocyte gene expression patterns distinguish subjects with and
without atherosclerosis
IlluminaCarotid atherosclerosis95Ex vivoGSE23746N/A
Deconvoluting Early Post-Transplant Immunity Using Purified Cell
Subsets Reveals Functional Networks Not Evident by Whole Blood
Analysis
AffymetrixKidney transplantation179Ex vivoGSE2422353
Cooperative and redundant signaling of leukotriene B4 and
leukotriene D4 in human monocytes
AffymetrixHealthy10In vitroGSE2486954
Gene expression profiling of the classical (CD14++CD16-),
intermediate (CD14++CD16+) and nonclassical (CD14+CD16+)
human monocyte subsets
IlluminaHealthy24Ex vivoGSE2591334
Direct Cell Conversion of Human Fibroblasts to Monocytic
phagocytes by Forced Expression of Monocytic Regulatory
Network Elements
Illumina Dermatomyositis15Ex vivoGSE27304N/A
cMyb and vMyb in human monocytesAffymetrixHealthy6In vitroGSE281655
Temporal transcriptional changes in human monocytes following
acute myocardial infarction: The GerMIFs monocyte expression study
IlluminaAcute myocardial
infarction
76Ex vivoGSE28454N/A
mRNA expression profiling of human immune cell subset (Roche)AffymetrixHealthy47Ex vivoGSE2849056
mRNA expression profiling of human immune cell subsets (HUG)AffymetrixHealthy33Ex vivoGSE2849156
Changes in gene expression profiles in patients with 5q- syndrome
in CD14+ monocytes caused by lenalidomide treatment
Illumina5q- syndrome17Ex vivoGSE31460N/A
Genome-wide analysis of lupus immune complex stimulation of
purified CD14+ monocytes and how this response is regulated by C1q
IlluminaHealthy8In vitroGSE3227857
Transcriptome analysis of circulating monocytes in obese patients
before and three months after bariatric surgery
IlluminaObesity48Ex vivoGSE3257558
CD4 on human monocytesAffymetrixHealthy6In vitroGSE3293959
Peripheral Blood Monocyte Gene Expression in Recent-Onset
Type 1 Diabetes
IlluminaType 1 Diabetes22Ex vivoGSE3344060
Traffic-related Particulate Matter Upregulates Allergic Responses
by a Notch-pathway Dependent Mechanism
AffymetrixHealthy16In vitroGSE34025N/A
Human monocyte activation with NOD2L vs. TLR2/1LAffymetrixHealthy45In vitroGSE3415661
Bacillus anthracis' lethal toxin induces broad transcriptional
responses in human peripheral monocyte
AffymetrixHealthy8In vitroGSE3440762
Gene expression profiles of human blood classical monocytes
(CD14++CD16-), CD16 positive monocytes (CD14+16++ and
CD14++CD16+), and CD1c+ CD19- dendritic cells
AffymetrixHealthy9Ex vivoGSE34515N/A
Genome-wide analysis of monocytes and T cells' response to
interferon beta
IlluminaHealthy12In vitroGSE3462763
Highly pathogenic influenza virus inhibit Inflammatory Responses
in Monocytes via Activation of the Rar-Related Orphan Receptor
Alpha (RORalpa)
AffymetrixHealthy12In vitroGSE35283N/A
Transcriptome profiles of human monocyte and dendritic cell subsetsIlluminaHealthy49Ex vivoGSE3545764
Influenza virus A infected monocytesIlluminaHealthy6In vitroGSE3547365
PGE2-induced OSM expressionAffymetrixChronic wound6Ex vivoGSE3699566
Inflammatory Expression Profiles in Monocyte to Macrophage
Differentiation amongst Patients with Systemic Lupus
Erythematosus and Healthy Controls with and without an
Atherosclerosis Phenotype
IlluminaSystemic lupus
erythematosus
72Ex vivoGSE37356N/A
New insights into key genes and pathways involved in the
pathogenesis of HLA-B27-associated acute anterior uveitis
AffymetrixAcute anterior uveitis6In vitroGSE37588N/A
Analysis of blood myelomonocytic cells from RCC patientsIlluminaRenal cell carcinoma8Ex vivoGSE3842467
Nanotoxicogenomic study of ZnO and TiO2 responsesIllumina Healthy90In vitroGSE39316N/A
Macrophage Microvesicles Induce Macrophage Differentiation
and miR-223 Transfer
AffymetrixHealthy24In vitroGSE4188968
TREM-1 is a novel therapeutic target in PsoriasisAffymetrixPsoriasis15In vitroGSE4230569
Comparison study between Uremic patient with Healthy controlAffymetrixChronic kidney disease6Ex vivoGSE43484N/A
Microarray analysis of IL-10 stimulated adherent peripheral blood
mononuclear cells
AffymetrixHealthy8In vitroGSE4370070
Monocytes and Dendritic cells stimulated by 13 human vaccines
and LPS
IlluminaVaccination128In vitroGSE4472136
Gene expression profile of human monocytes stimulated with
all-trans retinoic acid (ATRA) or 1,25a-dihydroxyvitamin D3 (1,25D3)
AffymetrixHealthy12In vitroGSE4626871
Transcriptome analysis of blood monocytes from sepsis patientsIlluminaSepsis44Ex vivoGSE4695572
Tumor-educated circulating monocytes are powerful specific
biomarkers for diagnosis of colorectal cancer
IlluminaColorectal cancer93Ex vivoGSE4775673
Similarities and differences between macrophage polarized gene
profiles
Illumina Healthy12In vitroGSE4924074
The effect of cell subset isolation method on gene expression in
leukocytes.
IlluminaHealthy50Ex vivoGSE50008N/A
Transcriptome analysis of HIV-infected peripheral blood
monocytes
IlluminaHIV86Ex vivoGSE5001175
Gene expression profiles in T-lymphocytes and Monocytes of
participants of the Tour de France 2005
AffymetrixHealthy66Ex vivoGSE5105N/A
Effects of exercise on gene expression level in human monocytesAffymetrixHealthy24Ex vivoGSE5183576
T helper lymphocyte- and monocyte-specific type I interferon (IFN)
signatures in autoimmunity and viral infection.
AffymetrixAutoimmune diseases36Ex vivoGSE5199777
Longitudinal comparison of monocytes from an HIV viremic vs
avirmeic state
AffymetrixHIV16Ex vivoGSE522078
Expression data from monocytes and monocyte derived
macrophages
AffymetrixHealthy12In vitroGSE52647N/A
Transcriptome analysis of primary monocytes from HIV+ patients
with differential responses to therapy
IlluminaHIV14Ex vivoGSE5290079
Human blood monocyte response to IL-17A in cultureAffymetrixHealthy6In vitroGSE54884N/A
Divergent genome wide transcriptional profiles from immune
cell subsets isolated from SLE patients with different ancestral
backgrounds
IlluminaSystemic lupus
erythematosus
208Ex vivoGSE5544780
Cell Specific Expression & Pathway Analyses Reveal Novel
Alterations in Trauma-Related Human T-Cell & Monocyte Pathways
AffymetrixTrauma patients42Ex vivoGSE558081
Immune Variation Project (ImmVar) [CD14]AffymetrixHealthy485Ex vivoGSE56034N/A
Transcriptomics of human monocytesIlluminaHealthy1202Ex vivoGSE5604582
Effect of vitamin D treatment on human monocyteAffymetrixHealthy16In vitroGSE56490N/A
Monocytes of patients with familial hypercholesterolemia show
alterations in cholesterol metabolism
AffymetrixHypercholesterolemia23Ex vivoGSE605483
Gene expression data from CD14++ CD16- classical monocytes
from healthy volunteers and patients with pancreatic ductal
adenocarcinoma
AffymetrixPancreatic ductal
adenocarcinoma
12Ex vivoGSE60601N/A
Activation of the JAK/STAT pathway in Behcet's DiseaseAffymetrixBehcet’s disease29Ex vivoGSE61399N/A
Alarmins MRP8 and MRP14 induce stress-tolerance in phagocytes
under sterile inflammatory conditions
IlluminaSterile inflammation12In vitroGSE61477N/A
GM-CSF induced gene-regulation in human monocytesAffymetrixHealthy6In vitroGSE6366284
Treatment of human monocytes with TLR7 or TLR8 agonistsAffymetrixHealthy9In vitroGSE6448085
Restricted Dendritic Cell and Monocyte Progenitors in Human
Cord Blood and Bone Marrow
IlluminaHealthy36Ex vivoGSE6512886
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 Vaccination20In vitroGSE65412N/A
Comparative analysis of monocytes from healthy donors, patients
with metastatic breast cancer, sepsis or tuberculosis.
IlluminaBreast cancer and
Bacterial infection
13Ex vivoGSE6551787
Expression data from intermediate monocytes from healthy donors
and autoimmune uveitis patients
AffymetrixAutoimmune uveitis21Ex vivoGSE6693688
Induction of Dendritic Cell-like Phenotype in Macrophages during
Foam Cell Formation
AffymetrixHealthy22In vitroGSE713889
Genome Wide Gene Expression Study of Circulating Monocytes in
human with extremely high vs. low bone mass
AffymetrixHealthy26Ex vivoGSE7158N/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
AffymetrixHealthy18Ex vivoGSE7264290
Expression data from monocytes of individuals with different
collateral flow index CFI
AffymetrixCoronary artery disease160Ex vivoGSE763891
Leukotriene D4 induces gene expression in human monocytes
through cysteinyl leukotriene type I receptor
AffymetrixHealthy8In vitroGSE780792
Gene expression profile during monocytes to macrophage
differentiation
AffymetrixHealthy9In vitroGSE8286N/A
Toll-like receptor triggering of a vitamin D-mediated human
antimicrobial response
AffymetrixHealthy50In vitroGSE892193
TRAIL Is a Novel Antiviral Protein against Dengue VirusAffymetrixDengue10In vitroGSE9378N/A
Gene Expression-Based High Throughput Screening: APL
Treatment with Candidate Compounds
AffymetrixLeukemia24Ex vivoGSE97694
Innate immune responses to TREM-1 activationAffymetrixHealthy11In vitroGSE998895

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 publication20. 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.

d550a47f-1497-46bc-a120-a60be2828d73_figure3.gif

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

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

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.

Comments on this article Comments (0)

Version 2
VERSION 2 PUBLISHED 07 Mar 2016
Comment
Author details Author details
Competing interests
Grant information
Copyright
Download
 
Export To
metrics
Views Downloads
F1000Research - -
PubMed Central
Data from PMC are received and updated monthly.
- -
Citations
CITE
how to cite this article
Rinchai D, Boughorbel S, Presnell S et al. A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2016, 5:291 (https://doi.org/10.12688/f1000research.8182.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.

Open Peer Review

Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 1
VERSION 1
PUBLISHED 07 Mar 2016
Views
38
Cite
Reviewer Report 21 Mar 2016
Ping Chen, National Eye Institute (NEI), National Institutes of Health, Besthesda, MD, USA 
David Kuo, National Eye Institute (NEI), National Institutes of Health, Bethesda, MD, USA;  University of California San Diego, La Jolla, CA, USA 
Approved with Reservations
VIEWS 38
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 ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Chen P and Kuo D. Reviewer Report For: A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2016, 5:291 (https://doi.org/10.5256/f1000research.8800.r12769)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 25 Apr 2016
    Darawan Rinchai, Systems Biology Department, Sidra Medical and Research Center, Doha, Qatar
    25 Apr 2016
    Author Response
    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 ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 25 Apr 2016
    Darawan Rinchai, Systems Biology Department, Sidra Medical and Research Center, Doha, Qatar
    25 Apr 2016
    Author Response
    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 ... Continue reading
Views
31
Cite
Reviewer Report 16 Mar 2016
Marc Pellegrini, Division of Infection and Immunity, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia 
Approved
VIEWS 31
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 ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Pellegrini M. Reviewer Report For: A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2016, 5:291 (https://doi.org/10.5256/f1000research.8800.r12768)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.

Comments on this article Comments (0)

Version 2
VERSION 2 PUBLISHED 07 Mar 2016
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
Sign In
If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

The email address should be the one you originally registered with F1000.

Email address not valid, please try again

You registered with F1000 via Google, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Google account password, please click here.

You registered with F1000 via Facebook, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Facebook account password, please click here.

Code not correct, please try again
Email us for further assistance.
Server error, please try again.