Version Changes
Revised. Amendments from Version 1
Per reviewers' comments we added background information about the subject matter (monocyte immunobiology), as well as details regarding the dataset selection and curation process. Figures 2 and 3 were also updated. The table orientation was changed, and we have also updated the title of this article.
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 1– 10. This in turn has provided opportunities for the discovery of biomarkers and for the development of novel therapeutic modalities 3, 11– 13. More recently such systems-scale profiling of the blood transcriptome has also been used to monitor response to vaccines or therapeutic drugs 14– 19. 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 the 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 (CD14++CD16-). The remaining 10% is divided into two subsets: intermediate monocytes with high expression of CD14 and CD16 (CD14++CD16+ or CD14+CD16+) and non-classical monocytes that express low levels of CD14 but high levels of CD16 (CD14dimCD16++ or CD14-CD16++) 32– 34. The factors that govern the migration of monocytes and roles that each subset plays during disease processes are not well understood. 1) In autoimmune diseases: Non-classical monocytes are regarded as crucial effectors in the pathogenesis of rheumatoid arthritis, ankylosing spondylitis 35, systemic lupus erythematosus (SLE) 36 and multiple sclerosis 37. This monocyte subset carries a distinct inflammatory signature in patients with SLE 36. Classical monocytes on the other hand have been shown to dominate the inflamed mucosa in Crohn’s disease 38. Skewing of monocytes towards the intermediate subset has been observed in patients with autoimmune uveitis and linked to administration of glucocorticoid therapy 39. 2) In cardiovascular diseases: circulating monocytes play a pivotal role by releasing cocktails of cytokines, factor and proteases that are involved in vascular growth 40. Monocyte subsets show functional and phenotypic changes in cardiovascular diseases. The accumulation of classical monocytes is for instance a hallmark of progression of atherosclerosis 41– 43. An association between intermediate monocytes and cardiovascular events has also been documented with this monocyte subset being proportionally elevated following myocardial infarction or atrial fibrillation 44, 45 or in at risk subjects 46. 3) In cancer: Intermediate monocytes are viewed as potential diagnostic indicators for colorectal cancer 47. Another study has shown that elevated abundance of intermediate monocytes is associated with survival of adult or childhood acute lymphoblastic leukemia 48. The changes of gene expression profiles in monocytes reveal high specificity for the tissue type and cancer histotype, and are induced in response to soluble factors released by the cancer cells in the primary or metastatic site 49. Moreover, monocytes, comprising the monocyte-myeloid-derived suppressor cells population, from patients with metastatic breast cancer resemble the reprogrammed immunosuppressive monocytes in patients with severe infections, both by their surface and functional phenotype but also by their gene expression profile 50. This signature of immunosuppression could therefore constitute a good biomarker for assessing disease progression. 4) In infections: monocytes are also key players in the immediate immune response to infectious agents as well as the subsequent development of the adaptive immune response 51. Given the importance of classical and intermediate monocytes in pathogenesis of infectious and other inflammatory disorders, delineation of their functional and phenotypic characteristics has been studied extensively. The response mounted by classical monocytes has emerged as being critical for the control of a wide range of infectious diseases, including infections caused by bacteria 52– 57, parasites 58 and fungi 59. In contrast, intermediate monocytes have been associated with pathologic immune responses against bacteria 60, 61 and parasites 62. In the context of HIV infection; CD14 expression is reduced on classical monocytes in chronically HIV-1 infected adults on anti-retroviral therapy 63, 64. Moreover, loss of CCR2 expressing non-classical monocytes is associated with cognitive impairment in antiretroviral therapy-naïve infected subjects 65. Altogether these findings indicate that monocytes are more than circulating precursors and have different effector functions in response to various infections and during inflammation. Clearly furthering our understanding of the role of monocyte subsets in health and disease will require many more studies, also we hope that the dataset compendium that we are making available to the research community via this publication can help support these endeavors.
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 66. 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. 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. The datasets cover a broad range of studies investigating human 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 descriptions of the studies 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++) 32– 34 [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 67 [GXB: GSE44721]. The datasets that comprise our collection are listed in Table 1 and can be browsed interactively in GXB.
Table 1. List of datasets constituting the collection.
Dataset upload and annotation on GXB
Once a final selection was 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 treatment 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). We have systematically verified that expression of the genes encoding those surface markers was consistent with grouping labels provided by depositors. 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 (expression of XIST should be high in females and low in males).
Data availability
The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2016 Rinchai D et al.
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). http://creativecommons.org/publicdomain/zero/1.0/
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 2; referees: 2 approved]
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