Letters

PscB A Browser to Explore Plant Single CellRNA-Sequencing Data Sets1[OPEN]

Dear EditorHigh-throughput single cell RNA sequencing (scRNA-

Seq) provides unprecedented power for understandinggene expression within complex tissues The develop-ment of droplet-based technologies (Klein et al 2015Macosko et al 2015 Zheng et al 2017) and the in-comparable depth of data it affords means thatscRNA-Seq has become a fundamental method fortranscriptomic analysis in metazoan studies With thefirst works applying this technology to plant tissuesrecently published (Denyer et al 2019 Jean-Baptisteet al 2019 Ryu et al 2019 Shulse et al 2019 Zhanget al 2019) this is also rapidly becoming the case inplants In anticipation of the proliferation of plant celldata sets a central browser with tailored visualizationand analysis tools is required for biologists to easilyinvestigate scRNA-Seq data at the exceptional reso-lution it offers The few current available browserspresent scRNA-Seq data in formats that describeexpression in broad terms of cell identity and cursorydevelopmental stage but fail to fully embrace theunique and complex spatiotemporal expression in-formation inherent (Supplemental Table S1)

To address this need we here present a web-basedgraphical user interface for easy but comprehensiveinterrogation of plant scRNA-Seq data (httpswwwzmbp-resourcesuni-tuebingendetimmermansplant-single-cell-browser) The Plant sc-RNA-SeqBrowser (PscB) was built using the shiny framework(Chang et al 2018) which translates user-drivenevents such as gene entries and button clicks into Rreactive objects to explore scRNA-Seq data using theSeurat and Monocle packages (Trapnell et al 2014Satija et al 2015) and displays results as dynamic webcontent Detailed expression data for individual genesor gene lists can be visualized and downloaded as (1)t-SNE plots showing expression profiles across thecluster cloud (Fig 1 AndashF) (2) violin plots describing thedistribution of expression values of cells within a givencluster (Fig 1G) and (3) tables summarizing quantita-tive information on gene expression across clusters(Fig 1H) In addition the highly resolved patternsof gene expression across pseudotime-derived develop-mental trajectories can be visualized as scatterplot graphsand heatmaps (Fig 2) The Browser also provides links toarchival databases and relevant publications and includes

a detailed customized tutorial to aid data interpretationbeyond the cluster level

With our recent scRNA-Seq data set as a starting point(Denyer et al 2019) the Browser facilitates the intuitiveassessment of gene expression across all cell types anddevelopmental stages of the Arabidopsis (Arabidopsisthaliana) root The nearly 5000 cells profiledmanifest in acluster arrangement that captures the developmentaldynamic of the root both with respect to developmentaltime and the spatial relationships between cell and tissuetypes The cluster map shows that cells of the niche andmeristem form a central core from which cells of differ-ent identities radiate in contrasting directions (Fig 1FDenyer et al 2019) This map provides an immediatedetailed view of a genersquos pattern of expression displayedin t-SNE and violin plots (Fig 1 D and G) Furthermoredownloadable tables summarize mean expression percell both as raw and Seurat-normalized expressionvalues (Satija et al 2015) as well as the percentage ofcells expressing a given gene in each cluster offeringquantitative information for gene expression compari-sons between clusters (Fig 1H)

The scRNA-Seq data however capture spatiotempo-ral expression information at a resolution far beyond theclusters Accordingly many genes show expression injust a subset of cells within a cluster often reflecting amore intricate distinction in the fate state or differenti-ation of cells (Fig 1D) For instance genes expressed inmeristematic cells of a given tissue type mark a specificregion in the meristem clusters positioned adjacent totheir mature counterpart (Fig 1D AT3G22120) Like-wise cells at a given phase of the cell cycle colocalize todefine subdomains within the meristem clusters Ac-companying the Browserrsquos visualization tools is a com-prehensive tutorial that provides guidance inunderstanding the intricate distinctions of expressionprofiles localized to subdomains within cell clusters

The scRNA-Seq data are particularly powerful inresolving progressions in gene expression connected todevelopmental time Pseudotime analysis on our rootdata set using the Monocle2 software (Trapnell et al2014) reveals cells of progressive maturity spanningout from the central niche and meristem core A tem-poral reference map aids in discerning differences inexpression connected to differentiation (Figs 1D and2A AT4G13390) Moreover the Browser provides asearchable format to interrogate gene expression acrossindividual developmental trajectories For example thestem cell-to-mature trichoblast atrichoblast or cortexcell trajectories reveal waves of gene expression guid-ing the development of these cell types that based onevidence from reporter lines are highly predictive ofrelative expression along the developing cell files(Denyer et al 2019) Upon a query heat maps will

1This work was supported by the Alexander von Humboldt-Stiftung (Humboldt Foundation to MT)

2Author for contact marjatimmermanszmbpuni-tuebingende3Senior authorXM TD andMCPT designed the project andwrote the article[OPEN]Articles can be viewed without a subscriptionwwwplantphysiolorgcgidoi101104pp2000250

464 Plant Physiology June 2020 Vol 183 pp 464ndash467 wwwplantphysiolorg 2020 American Society of Plant Biologists All Rights Reserved wwwplantphysiolorgon October 23 2020 - Published by Downloaded from

Copyright copy 2020 American Society of Plant Biologists All rights reserved

Figure 1 The scRNA-Seq Browser interface A Individual genes (up to four genes) can be queried B Alternately a gene list (up to 20genes) can be uploaded CData sets that include or exclude genes induced by the protoplasting procedure can be queriedD For genesdetected in a data set t-SNE plots are displayed showing expression across the clusters of cells This can be localized to a defined subsetof cellswithin a cluster E t-SNE plots are downloadable as a png file F Basic clustermaps aidwith quick initial interpretation GGeneexpression values across clusters can be visualized as violin plots depicting the distribution of cellswith a given expression value in eachcluster The y axis shows the gene expression level and the x axis represents the proportion of cells showing a given expression valueHIn addition histograms showing mean expression per cell for a given gene across clusters either as raw UMI (unique molecularidentifier) counts or as Seurat-normalized data (not shown here) and percentages of cells expressing are displayed Violin plots aredownloadable as png files and expression data as comma-separated files C Cluster corresponding cluster identities are shown in F

Plant Physiol Vol 183 2020 465

Letters

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display the expression dynamics of relevant genes along atrajectory (Fig 2B) These are accompanied by a heat mapdisplaying all genes dynamically expressed across pseu-dotime for simple comparison Alongside these heatmaps scatterplot graphs are produced depicting expres-sion dynamics for genes of interest along pseudotime atindividual-cell resolution (Fig 2C) Together this in-formation can be used to assess the precise temporalgene expression changes that cells undergo duringthe transition from stem cell through differentiation

Several challenges such as cell size and osmoticpressure sensitivities are concerns for scRNA-Seq ap-plications to plant tissues However perhaps the mostprominent concern for plant biologists approachingscRNA-Seq is the process of producing viable protoplastsand the downstream transcriptional effects of this proto-plasting procedure The scRNA-Seq Browser uniquelypresents the data in two formats one where genes in-duced during the protoplasting procedure are removedand one where they are not While the inclusion of genesinduced by the procedure appears to have little effect onclustering applications such as marker selection pseu-dotime analysis and gene network development fromdevelopmental trajectories appear heavily influenced

In summary to address the obvious need for dataaccessibility tools that can navigate the numerouscomplex scRNA-Seq data sets ahead we developed acurated Plant Single Cell RNA-Sequencing Browser thatallows biologists to quickly and easily explore gene ex-pression at a variety of desired depths Besides the im-mediate view of a genersquos pattern of expression obtainedfrom t-SNE and violin plots the Browser offers theunique opportunity to (1) gain quantitative informationon gene expression across clusters (2) visualize highlyresolved patterns of expression along pseudotime-derived trajectories and (3) discern the intricacies ofspatiotemporal gene expression information capturedwithin clusters Additional distinguishing features ofthis Browser are listed in Supplemental Table S1

To best serve the community we invite colleagues toutilize this platform to make their scRNA-Seq datapublicly available in this highly interpretable format Todo so please contact the corresponding author As eachscRNA-Seq atlas particularly those from tissues otherthan the root will contain new intricacies in their clusterarrangement customized detailed tutorials such as theone accompanying the current root data set are a ne-cessity for comprehensive appreciation of the full datainherent The Browser can be modified and updated toincorporate future analysis and visualization tools ThePlant Single Cell RNA-Sequencing Browser will thusprovide an up-to-date and rich centralized resource forplant research with the interpretative guidance requiredfor properly deciphering this rich data format

Supplemental Data

The following supplemental materials are available

Supplemental Table S1 Plant single cell RNA-Seq browsers

Figure 2 The scRNA-Seq Browser resolves gene expression connectedto developmental time A Temporal reference map derived frompseudotime analysis of all cells aids in linking expression to stages ofdifferentiation B and C Gene expression along individual develop-mental trajectories provides additional information on temporal ex-pression dynamics associated with differentiation B An expressionheat map of selected genes dynamically expressed across a pseudotimetrajectory is produced The heat map is oriented with expression inmeristematic cells to the left and in mature cells to the right C Scat-terplots depicting expression dynamics for genes at individual cellresolution along pseudotime are simultaneously produced Heat mapsand scatterplot graphs are downloadable as png files

466 Plant Physiol Vol 183 2020

Letters

wwwplantphysiolorgon October 23 2020 - Published by Downloaded from Copyright copy 2020 American Society of Plant Biologists All rights reserved

ACKNOWLEDGMENTS

We thank Dieter Steinmetz and Andreas Keck for installation and mainte-nance of the server and Shreyas Meda Simon Klesen and Effie Symeonidi forconstructive feedback on browser design

Received February 28 2020 accepted March 16 2020 published March 242020

Xiaoli MaORCID ID 0000-0002-7078-3472

Center for Plant Molecular BiologyUniversity of Tuumlbingen 72076 Tuebingen Germany

Tom DenyerORCID ID 0000-0001-7413-9855

Center for Plant Molecular BiologyUniversity of Tuumlbingen 72076 Tuebingen Germany

Marja CP Timmermans23

ORCID ID 0000-0003-1931-9209Center for Plant Molecular Biology

University of Tuumlbingen 72076 Tuebingen Germany

LITERATURE CITED

Chang W Cheng J Allaire J Xie Y McPherson J (2018) Shiny Web ap-plication framework for R R package version 120 httpsCRANR-projectorgpackage5shiny

Denyer T Ma X Klesen S Scacchi E Nieselt K Timmermans MCP (2019)Spatiotemporal developmental trajectories in the Arabidopsis root

revealed using high-throughput single-cell RNA sequencing Dev Cell48 840ndash852e5

Jean-Baptiste K McFaline-Figueroa JL Alexandre CM Dorrity MWSaunders L Bubb KL Trapnell C Fields S Queitsch C Cuperus JT(2019) Dynamics of gene expression in single root cells of Arabidopsisthaliana Plant Cell 31 993ndash1011

Klein AM Mazutis L Akartuna I Tallapragada N Veres A Li V PeshkinL Weitz DA Kirschner MW (2015) Droplet barcoding for single-celltranscriptomics applied to embryonic stem cells Cell 161 1187ndash1201

Macosko EZ Basu A Satija R Nemesh J Shekhar K Goldman M TiroshI Bialas AR Kamitaki N Martersteck EM et al (2015) Highly parallelgenome-wide expression profiling of individual cells using nanoliterdroplets Cell 161 1202ndash1214

Ryu KH Huang L Kang HM Schiefelbein J (2019) Single-cell RNA se-quencing resolves molecular relationships among individual plant cellsPlant Physiol 179 1444ndash1456

Satija R Farrell JA Gennert D Schier AF Regev A (2015) Spatial re-construction of single-cell gene expression data Nat Biotechnol 33495ndash502

Shulse CN Cole BJ Ciobanu D Lin J Yoshinaga Y Gouran M TurcoGM Zhu Y OrsquoMalley RC Brady SM et al (2019) High-throughputsingle-cell transcriptome profiling of plant cell types Cell Rep 272241ndash2247e4

Trapnell C Cacchiarelli D Grimsby J Pokharel P Li S Morse M LennonNJ Livak KJ Mikkelsen TS Rinn JL (2014) The dynamics and regu-lators of cell fate decisions are revealed by pseudotemporal ordering ofsingle cells Nat Biotechnol 32 381ndash386

Zhang TQ Xu ZG Shang GD Wang JW (2019) A single-cell RNA se-quencing profiles the developmental landscape of Arabidopsis root MolPlant 12 648ndash660

Zheng GXY Terry JM Belgrader P Ryvkin P Bent ZW Wilson RZiraldo SB Wheeler TD McDermott GP Zhu J et al (2017) Massivelyparallel digital transcriptional profiling of single cells Nat Commun 814049

Plant Physiol Vol 183 2020 467

Letters

wwwplantphysiolorgon October 23 2020 - Published by Downloaded from Copyright copy 2020 American Society of Plant Biologists All rights reserved