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Single‐cellRNA‐seqtiesmacrophage

polarizationtogrowthrateofintracellularSalmonella

Antoine‐EmmanuelSaliba1,2,LeiLi2,AlexanderJ.Westermann1,SilkeAppenzeller2†,DaphneA.C.Stapels3,LeonN.Schulte1,SophieHelaine3,JörgVogel1,

1RNA Biology group, Institute for Molecular Infection Biology, University ofWürzburg, Josef‐Schneider‐Straße2,D‐97080Würzburg,Germany

2CoreUnitSysMed,UniversityofWürzburg,Josef‐Schneider‐Straße2,D‐97080Würzburg,Germany

3SectionofMicrobiology,MedicalResearchCouncil(MRC)CentreforMolecularBacteriologyandInfection,ImperialCollegeLondon,ArmstrongRoad,LondonSW72AZ,UK

†Presentaddress:ComprehensiveCancerCenterMainfranken,UniversityofWürzburg,AmHubland,D‐97074Würzburg

Correspondanceandmaterialrequest:joerg.vogel@uni‐wuerzburg.de

Thisfilecontains:SupplementaryFigures1to12Supplementaryreferences

© 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

SUPPLEMENTARY INFORMATIONARTICLE NUMBER: 16206 | DOI: 10.1038/NMICROBIOL.2016.206

NATURE MICROBIOLOGY | www.nature.com/naturemicrobiology 1

Supplementaryfigures

Supplementary Figure 1 | Characterization of bacterial input for infection and of

bystander cells.a, Representative histogram of GFP (left) andmCherry (right)measured byflowcytometryintensitiesoftheS.TyphimuriumSL1344WT(up)andSL1344pFCcGi(down).

Totalnumberofbacteriaanalyzedn=100,000.b,PercentageofSalmonellaexpressingGFPandmCherryinWT,GFP+andpFCcGistrains.Errorbars(S.D.)arecalculatedonthreeindependentbiologicalreplicates.c,qRT‐PCRdataofonebacterialandonemammalianhouse‐keepinggeneapplied to a bacterial culture (500 µL, OD600 of 2.0), naïve macrophages (10,000 cells),bystanders (10,000 cells) and infected macrophages (10,000 cells). Error bars (S.D.) arecalculatedontwoindependentbiologicalreplicates.Cq:quantificationcycle.

SupplementaryFigure2|Intracellularbacterialgrowthoverthecourseoftheinfection.

Representative time‐course of flow cytometry scatter plots of non‐infected (mock) andchallengedmacrophages. Gate 1 captures naïvemacrophages and bystanders, gate 2 infectedmacrophageswithasinglebacterium,andgate3macrophagesinfectedwithbacteriathathaveproliferated(numberofcellsdisplayedbypanel:Mock:3,950cells;0h:9,375cells;6h:5,627cells;12h:3,283cells;18h:5,460cells).

SupplementaryFigure3 |Technical assessmentof single‐cellRNA‐seq. a, Mapped readsclassificationbytheirclassesoverallthecellsthatpasstheinitialfilter(n=60).Errorbars(S.D.)arecalculatedonallcells.b‐c,Assessmentofthedynamicrangeandthesensitivityofthesingle‐cellRNA‐seqprotocol.Averagenormalizedreadcounts(b)andaveragedetectionrate(thatis,the probability to have a read count value above 0) (c) for the 92 ERCC RNA spike‐in as afunctionofthenumberofRNAmoleculesacrossall thesingle‐cellsthatpasstheinitialqualitycontrol (n=60 cells,SupplementaryTable1).d, Identification of ‘biologically’ variable genesusingspike‐instomodelthetechnicalnoise.Coefficientofvariation(CV2)isplottedagainsttheread counts for all the naïve and challenged cells. In blue, average values for ERCC spike‐ins(blue square) are fitted to a parametrized model1 (Brennecke et al, 2013, PMID: 24056876)(solid blue curve). Variable genes (red circles) among all genes (black circles) are labeled,

respectively. Supporting that bone‐marrowmacrophages are fullydifferentiated,wevalidatedthatcell‐cycledependentgenes(greendots)arenotvariable.

SupplementaryFigure4 |Single‐cellclustering.a,t‐SNEanalysisofall60cells.Eachcell isrepresentedasadotandcolorsindicatecellularidentitiesasinferredfromtheoriginalFC(flowcytometry) gates.b,Gap statistic of k‐means clustering using the RaceID algorithm. The firstlocal maximum (k=3, indicated with an arrow) provides a good estimate for the number ofclusters thatachievesoptimalseparationof thedata intosubpopulations.Thedatapointsanderror bars refer to the average and standard deviation of gap statistics among 50 bootstrapsamples.

Supplementary Figure 5 | PAGODA analysis applied to naïve and challenged cells. Thedendrogram shows the overall clustering of all 60 cells. Every cell is related to the flowcytometry sorting gate (and its correlated bacterial growth status) and is associated to itscorrespondingcellgroupasdefinedinFig.2b.TheCellPCscoreheatmapbelowreflectsthetop4aspectsofheterogeneity(P<0,05)detectedbyPAGODAandeveryaspect isassociatedtoGOtermsnotedasrowlabels.Finally,everyheterogeneityaspectisassociatedwithunderlyinggenesets.

Supplementary Figure6 |Principal component analysis (PCA) applied tomacrophages

withnon‐growingandgrowingbacteria.PCAofallvariablegenesamongmacrophageswithgrowing (14 cells) and non‐growing bacteria (16 cells) (as inferred from the original flowcytometry gates (Fig. 1b)) is performed. In this analysis, bystanders and naïve cells are nottakenintoaccount.Thecontributionofeachcell(dot)tothefirsttwodimensionsisplottedwithcolorreferringtoinitialcellularidentity.

Supplementary Figure 7 | Single‐cell differential gene expression (SCDE) approach

definesgeneclustersspecific forPCA‐isolatedcellgroups.Cellgroups I, II and III isolated

from PCA analysis (Fig. 2b) were compared in pairwise manner using the SCDE method2(Kharchenkoetal,2014,PMID:24836921)usingallgenesthatpassedqualitycontrol(QC)(seeMethods). All genes with P<0.01 (‘threshold’) were selected for downstream analysis andrepresented in a heatmap (Fig.2c).a,ClustersA andB are derivedby comparingnaïve cellsversuschallengedcells, labelledrespectivelycell‐groups Iand IIplus IIIon thePCAmap(Fig.2b).b,ClustersCandDarederivedbycomparingcell‐groups II and III identifiedon thePCAmap(Fig.2b).

SupplementaryFigure8 |Bimodal/sporadicproinflammatorygeneexpression ingroup

II.a,OnthePCAmapondimensions(Dim)1and2ofallnaïveandchallengedcells(Fig.2b),single‐cellgeneexpressionofTlr2andNlrp3arecolor‐codedshowingpreferenceexpressioninthegroupII.b,ViolonplotshowingtheexpressionofTnf,Nlrp3,andCxcl10inGroupII(Fig.2b;28cells).

Supplementary Figure9 |Gene expressionpattern inM2‐like cells.On the PCAmap ondimensions (Dim)1and2of allnaïveandchallengedcells (Fig.2b, 60 cells), single‐cell geneexpression of Mrc1, Ccl8, Spp1, Id1, Timp1 and Gpr35 are color‐coded showing preferedexpressioningroupIII.

Supplementary Figure 10 | Independent confirmation of population segregation. Areplicateexperimentofinfection,cellsortingandsingle‐cellRNA‐seqwasperformed.PCAofallcells that passed the technical filter (18 naïve macrophages, 23 bystanders, 20 macrophagescontainingnon‐growingbacteria, and20macrophages containinggrowingbacteria) isplottedonthefirsttwodimensions.

Supplementary Figure 11 | Kinetics of the levels of IL4RA (CD124) during infection.

Macrophageswere left uninfected or infectedwithSalmonella for 20h. Cellswere recovered,labeled for IL4RA (CD124) and analyzed by flow cytometry at 0, 2, 6, 10 or 20 h (n >30,000cells). From 10 h onwards, cells were separated in the different macrophage populationscontaining growing or non‐growing bacteria. Error bars (S.D.) are calculated on twoindependentbiologicalreplicates.

Supplementary Figure 12 | Macrophage polarization depends on bacterial growth.

Macrophages were infected with Salmonella for 20 h prior to being recovered, labeled andanalyzedbyflowcytometry(n>30,000cells).Cellsweregateddependingonthegrowthstatusof the bacteria and separated in cells containing multiple non‐growing bacteria or cellscontaininggrowingbacteria.LevelsofdetectionofIL1B,ARG1,IL4RA(CD124)andCD86weremeasured and compared in the different macrophage populations. Error bars (S.D.) arecalculatedon the indicatedn independentbiological replicates.A two‐tailedone sample t‐testwasappliedtoobtainP.

Supplementaryreferences

1  Brennecke, P. et al. Accounting for technical noise in single‐cell RNA‐seq experiments. Nat Methods 10, 1093‐1095, (2013). 

2  Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single‐cell differential expression analysis. Nat Methods 11, 740‐742, (2014).