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‘LungGENS’: a web-based tool for mappingsingle-cell gene expression in the developing lungYina Du,1 Minzhe Guo,1,2 Jeffrey A Whitsett,1 Yan Xu1,3

1Department of Perinatal andPulmonary Biology, ThePerinatal Institute and Sectionof Neonatology, CincinnatiChildren’s Hospital MedicalCenter, Cincinnati, Ohio, USA2Department of ElectricalEngineering and ComputingSystems, University ofCincinnati, Cincinnati, Ohio,USA3Division of BiomedicalInformatics, CincinnatiChildren’s Hospital MedicalCenter, Cincinnati, Ohio, USA

Correspondence toDr Yan Xu, Divisions ofPulmonary Biology, PerinatalInstitute and BiomedicalInformatics, CincinnatiChildren’s Hospital MedicalCenter, 3333 Burnet Avenue,MLC7029, Cincinnati,OH 45229-3039, USA;Yan.Xu@cchmc.org

Received 11 March 2015Revised 5 May 2015Accepted 9 June 2015

To cite: Du Y, Guo M,Whitsett JA, et al. ThoraxPublished Online First:[please include Day MonthYear] doi:10.1136/thoraxjnl-2015-207035

ABSTRACTWe developed LungGENS (Lung Gene Expression iNSingle-cell), a web-based bioinformatics resource forquerying single-cell gene expression databases byentering a gene symbol or a list of genes or selecting acell type of their interest. Gene query providesquantitative RNA expression of the gene of interest ineach lung cell type. Cell type query returns associatedselective gene signatures and genes encoding cellsurface markers and transcription factors in interactiveheatmap and tables. LungGENS will be broadlyapplicable in respiratory research, providing a cell-specificRNA expression resource at single-cell resolution.LungGENS is freely available for non-commercial use athttps://research.cchmc.org/pbge/lunggens/default.html.

INTRODUCTIONThe lung is a complex multicellular organ com-posed of a diversity of distinct cell types that inter-act to accomplish lung morphogenesis andfunction. Knowledge regarding the proliferation,differentiation and functions of individual cells andthe mechanisms by which cells interact to form thelung provides insight into the processes underlyinglung morphogenesis, function and repair. Recentadvances in single-cell isolation and massive parallelDNA sequencing enable resolution of gene expres-sion in individual cells, providing insight into thediversity of cell types, and gene networks directingcell differentiation, and the complex interactionsamong diverse cell types. We used massive parallelDNA sequencing and an unbiased analyticapproach to identify major cell types and themRNA signatures in single cells isolated from theentire embryonic mouse lung at the saccular phaseof morphogenesis, a time of active proliferationand differentiation (E16.5) before birth. The dataprovide a rich knowledge base, identifying uniquecontributions made by multiple pulmonary celltypes, and the biological processes mediating for-mation and function of the lung prior to birth(M Guo, et al: SINCERA: A pipeline for single-cellRNA-Seq profiling analysis, submitted). Processingand interpreting the extensive RNA data generatedat single-cell level in an entire organ present amajor analytical challenge. There are few readilyaccessible web tools or databases to facilitate queryand visualisation of such complex gene expressionpatterns. In order to facilitate access of single-celltranscriptomic data and visualise the complex data,we developed LungGENS (Lung Gene ExpressioniN Single-cell), a web tool useful for mapping geneexpression patterns in specific pulmonary cells at

single-cell level. The current version of LungGENSwas built using the lung single-cell RNA-seq datafrom mouse lung at embryonic day, E16.5. Theprogramme and website will be extended forexpression data from ongoing studies of the mouseand human lung during development as they arecompleted by the LungMAP consortium and otherinvestigators interested in lung biology.

METHODSLungGENS was developed in Eclipse (http://www.eclipse.org/), a Java IDE (integrated developmentenvironment). Specifically, HTML5, JavaScriptand Java programming languages were used forLungGENS web page and server development. Weused JSON ( JavaScript Object Notation) tosupport an interchangeable data structure for theseprogramming languages, making data transmissionbetween a server and web application easy and lan-guage independent.MYSQL, a relational database management

system, serves as one central component of the webtool by managing data storage and retrieval. In thedatabase, LungGENS data sets were divided intoseveral relational data tables with gene names andcell types as primary keys. This design guaranteesthe efficiency and accuracy of data querying andtable operation. Relational data tables includeRNA-Seq gene expression, FPKM (Fragments PerKilobase of Exon Per Million Fragments Mapped)or raw counts, normalised expression (Z-scoretransformed), gene summary, gene correlations, cellselective signature genes, surface markers and asso-ciated transcription factors.To provide interactive data visualisation,

Highchart (http://www.highcharts.com) was appliedduring web tool development. Highchart is com-patible in both modern mobile and desktop brow-sers (eg, Safari and Chrome). LungGENS usedcombinations of heatmap, histogram and bar graphto display gene expression for individual cells andstatistical results. Profile charts (line chart) wereused to display RNA expression profiles after entryof a query gene and its 20 most highly correlatedgenes that may share functional similarity with thequery gene.

RESULTSLungGENS is an open access tool that can be freelyaccessed at https://research.cchmc.org/pbge/lunggens/default.html. The search tool is easy to use and offerstwo distinct interfaces that facilitate user-definedqueries: ‘Query by gene’ and ‘Query by cell type’ asdepicted in figure 1.

Du Y, et al. Thorax 2015;0:1–3. doi:10.1136/thoraxjnl-2015-207035 1

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‘Query by gene’ allows users to input a gene symbol of interest.Query output consists of five sections:‘Identifications’ (‘IDs’) section: LungGENS provides externallinks by mapping query gene to benchmark databases andresources (NCBI, GeneCard, MGI, Ensembl and ProteinAtlas). By redirecting users to these authoritative databases,users can readily incorporate external knowledge to gain amore comprehensive understanding of the gene of interest.‘Summary’ section: LungGENS displays query gene expressionfrom 148 single cells isolated from whole lung digests fromfetal mice at E16.5 using heatmap and individual bar graphs.The programme provides mean expression and t test p values(negative log-transformed) of the query gene in nine distinctcell types using histograms. LungGENS highlights the cell typein which the query gene is significantly more highly expressedin red colour. The highlighted cell type is a ‘clickable’ hyper-link which will redirect users to ‘Query by cell type’ page.Cells with highest and the second highest levels of expressionare identified.‘Cell Type Statistics’ section: LungGENS summarises cell typestatistics for the queried gene, including maximum, minimum,average expression, the number and identity of cells expressingthe RNA and the percentage of cells expressing it in each celltype.‘Top 20 Correlated Genes’ section: LungGENS retrieves20 most closely correlated genes across 148 cells from precom-piled correlation tables for each query gene. Data arepresented as a group of profiles in charts, the query genebeing highlighted in red. A table lists the 20 most correlatedgenes, their correlation coefficient and their mean expressionacross nine cell types is shown using histograms. Users canfurther examine the closely correlated genes by clicking thegene symbol/ID or after downloading the table for furtheranalysis.

‘Dynamic Gene Expression’ section: LungGENS redirectsusers to ‘Lung Developmental Gene Expression Profiles’, aweb tool we built using lung developmental time course RNAmicroarray data. This database enables users to query dynamicRNA expression profiles during prenatal–perinatal lung devel-opment in three mouse strains A/J, C57BL/6J (B6) andSwiss-Webster Strain, visualised as interactive line graphs.1 2

The relative expression of the query gene across 52 differenttissues was displayed as bar graphs.3

The current version of LungGENS supports both ‘Query bysingle gene’ and ‘Query by gene list’. ‘Query by gene list’ allowsthe user to type in or paste their list of genes of interest to iden-tify cell types that selectively express in that set of gene. Thesearch engine will retrieve the expression profile of each gene inthe list across all cell types in the database (E16.5 data in currentversion) and use t test comparison of the gene among various celltypes. The cell type distribution is then depicted using a pie chartthat represents percentage of genes selectively expressed in eachcell type with a t test p value <0.05. Genes with a t test pvalue>0.05 are defined as unselectively expressed. The corre-sponding heatmaps and gene lists associated with each cell typeare provided. Functional enrichment of the gene set can be per-formed by submitting the list to ‘Toppgene Suite’, anotherCincinnati Children’s Hospital Medical Center (CCHMC) devel-oped web tool (https://toppgene.cchmc.org/).4 LungGENS pro-vides the interaction and redirection functions for users to selectsignature genes or correlated genes from LungGENS and directlyimport them into Toppgene Suite for analysis.

Query by cell type‘Query by cell type’ enables users to select one of nine prede-fined cell types (identified by unsupervised hierarchical cluster-ing followed by functional analysis and biomarker validation)(M Guo, et al, submitted) to query the database. For each celltype of interest, the web tool will provide relatively selective

Figure 1 LungGENS (Lung Gene Expression iN Single-cell) provides two main queries: ‘Query by gene’ and ‘Query by cell type’.

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gene signatures, associated cell surface markers and transcriptionfactors. Featured genes are visualised in an interactive heatmapwhich enables users to compare gene expression across all indi-vidual cells in the nine major cell clusters. Each heatmap is asso-ciated with a data table containing Gene symbol, Ensemble ID,t test p value (the lower the p value, the more selective is theassociated RNA with the query cell type) and mean expressionacross nine cell types. Gene symbol and Ensemble ID columnswere designed with hyperlinks to enable users to redirect thecell type query back to the gene query page in LungGENS.LungGENS provides a search box associated with each table,which enables users to identify if their gene of interest is a ‘sig-nature gene’ that is selective for a given cell type. Both heatmapand tables can be downloaded.

In addition to the E16.5 single-cell RNA data generated atCCHMC, single-cell RNA-seq from mouse lung epithelial cellsat E18.5 published by Treutlein et al,5 can be searched inLungGENS. Users can select ‘Explore cell type (Mouse E18.5Epithelium)’ under ‘New Query Functions’ or select ‘E18.5(Mouse-Epi)’ in the drop down menu listed under the ‘Explorecell type’ function. After selection of one of four epithelial sub-types (ie, Club Cells, Ciliated Cells, Alveolar Type I and Type IIcells), LungGENS will display a heatmap and bar graphs repre-senting the relative expression of each signature gene for each ofthe four epithelial cell types. Since we expect that more lungsingle-cell data will be produced by the research community, wewill add new expression data sets as they are published andaccessible.

CONCLUSIONBased on our knowledge, there is no readily available web toolto enable users to (1) input a gene of their interest for identifica-tion of lung cell types expressing the gene or (2) input a celltype of interest to identify ‘signature’ genes, surface markersand transcription factors that are selectively expressed in variouslung cell types. LungGENS is designed to facilitate the retrievalof lung cell-specific gene expression information from extensivedata sets derived from RNA sequencing of single cells and to

integrate to the data with previous RNA expression studies frommouse lung at various developmental times.

LungGENS was developed on behalf of the NationalInstitutes of Health, Heart Lung Blood Institute ‘LungMAP’research project. The initial phase of the web development wasbased on RNA-seq data obtained from single cells isolated fromfetal mouse lung at E16.5. The current LungGENS database willbe naturally extended to ongoing data generated from normaland abnormal lung tissues and cells from additional species,including human, at additional developmental time points.

Twitter Follow Yan Xu at @yanxubao

Acknowledgements The authors gratefully acknowledge the open source chartinglibrary ‘Highchart’ developed by Highsoft AS company.

Contributors YX and JAW designed and supervised the web application; YDdeveloped the database and web application; MG and YX contributed to dataanalysis and interpretation and all authors contributed to the writing and revision ofthe manuscript.

Funding National Heart, Lung, and Blood Institute (NHLBI) U01 HL122642(LungMAP) and R01 HL105433.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement The web tool is freely available for non-commercial useat https://research.cchmc.org/pbge/lunggens/default.html. The E16.5 RNA-seqsingle-cell data are publicly available at https://research.cchmc.org/pbge/sincera.html#downloads.

REFERENCES1 Xu Y, Wang Y, Besnard V, et al. Transcriptional programs controlling perinatal lung

maturation. PLoS ONE 2012;7:e37046.2 Mariani TJ, Reed JJ, Shapiro SD. Expression profiling of the developing mouse lung:

insights into the establishment of the extracellular matrix. Am J Respir Cell Mol Biol2002;26:541–8.

3 Su AI, Wiltshire T, Batalov S, et al. A gene atlas of the mouse and humanprotein-encoding transcriptomes. Proc Natl Acad Sci USA 2004;101:6062–7.

4 Chen J, Bardes EE, Aronow BJ, et al. ToppGene Suite for gene list enrichmentanalysis and candidate gene prioritization. Nucleic Acids Res 2009;37(Web Serverissue):W305–11.

5 Treutlein B, Brownfield DG, Wu AR, et al. Reconstructing lineage hierarchies of thedistal lung epithelium using single-cell RNA-seq. Nature 2014;509:371–5.

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