OSCR

Odorant receptor coexpression and multi-expression in the dengue mosquito.

Code ↔ Paper

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  1. [1] § Methods › THSN and OSN renormalization and clustering ↔ AllNeuron_analyses_v4.R, lines 1–45 · score 0.81 · nompC, OSN cluster, Ir93a, junk, UMAP, mechanosensory
  2. [2] § Results › Twelve small subpopulations of candidate heat and humidity sensors ↔ THSN_analyses_v4.R, the whole file · a weak match · score 0.74 · Ir21a, Ir40a, hygrosensory neurons, Ir93a, THSNs, thermo
  3. [3] § Methods › Genome reannotation ↔ orcoMinusOSN_analyses_v8.R, lines 1–42 · score 0.70 · Drosophila orthologs, AaegL5, mosquito genes, renaming, grep, gtf
  4. [4] § Methods › Data preprocessing, ambient RNA decontamination and doublet removal ↔ THSN_analyses_v4.R, the whole file · a weak match · score 0.69 · CellRanger, pre clustering, Seurat, libraries, dims, UMAPs
  5. [5] § Methods › Genome reannotation ↔ AllNeuron_analyses_v4.R, lines 1–45 · score 0.67 · Drosophila orthologs, AaegL5, mosquito genes, grep, gtf, match
  6. [6] § Methods › Quantification of co-receptor expression in all neuron clustering ↔ orcoMinusOSN_analyses_v8.R, lines 44–87 · score 0.64 · co receptor, Ir8a, Ir76b, Ir25a, cutoff, break
  7. [7] § Methods › Quantification of co-receptor expression in all neuron clustering ↔ orcoPlusOSN_analyses_v7.R, lines 42–95 · score 0.64 · co receptor, Ir8a, Ir76b, Ir25a, cutoff, break
  8. [8] § Results › Segregated expression of ligand-specific ORs and IRs ↔ AllNeuron_analyses_v4.R, lines 47–108 · score 0.62 · OSN clusters, Ir8a, Ir76b, Ir25a, coreceptor, Ligand
  9. [9] § Methods › Data preprocessing, ambient RNA decontamination and doublet removal ↔ orcoMinusOSN_analyses_v8.R, lines 1–42 · score 0.58 · CellRanger, pre clustering, dims, Seurat, UMAPs, match
  10. [10] § Methods › Receptor gene tree inference ↔ orcoMinusOSN_analyses_v8.R, lines 205–239 · score 0.57 · cophenetic.phylo, Phylogenetic distances, trees, IR
  11. [11] § Methods › Receptor gene tree inference ↔ orcoPlusOSN_analyses_v7.R, lines 205–255 · score 0.56 · cophenetic.phylo, Phylogenetic distances, trees
  12. [12] § Methods › Quantification of receptor expression in THSN, orco + OSN, and orco- OSN clusters ↔ orcoPlusOSN_analyses_v7.R, lines 42–95 · score 0.55 · median log2FC, subtracting, breaks, thresholds, OSNs, orco
  13. [13] § Methods › Quantification of receptor expression in THSN, orco + OSN, and orco- OSN clusters ↔ orcoMinusOSN_analyses_v8.R, lines 44–87 · score 0.53 · median log2FC, subtracting, breaks, thresholds, orco, OSNs
  14. [14] § Methods › Quantification of receptor expression in THSN, orco + OSN, and orco- OSN clusters ↔ orcoMinusOSN_analyses_v8.R, lines 136–198 · score 0.52 · Pearson correlations, co expression, orco, OSN, clusters, receptor
  15. [15] § Methods › Quantification of receptor expression in THSN, orco + OSN, and orco- OSN clusters ↔ orcoPlusOSN_analyses_v7.R, lines 143–198 · score 0.52 · Pearson correlations, co expression, orco, OSN, clusters, receptor
  16. [16] § Methods › Genome reannotation ↔ Reannotation_ThreePrimeUTR_Extender.py, lines 467–472 · score 0.51 · gff3sort.pl, gffread, UTRs, gtf, reannotation

Paper

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The authors' code

R · 240 lines · 14 KB · no license · 6 matches

  1. library(Seurat)
  2. library(viridis)
  3. library(plotrix)
  4. library(scCustomize)
  5. library(scales)
  7. # LOAD pre-clustered orco- OSN data object
  9. # Navigate to directory containing the R object THSN_Seurat_object.Rdata
  10. load("orcoMinusOSN_Seurat_object.Rdata")
  11. MyOrcoMinusOSNs <- IR_Seurat
  12. total_droplets<-ncol(MyOrcoMinusOSNs) # 12,243 droplet dataset
  13. clustorder=c("0","7","13","2","10a","10b","1","17","6","12","15","3","16","11","5","4","9","14a","14b","8")
  15. # Fig. 3A and S11: Visualize UMAPs for clusters, batches, and key marker genes
  17. # Note that the AaegL5 gtf annotation used for the CellRanger alignments underlying these data uses
  18. # "OrX", "IrX", "GrX" names for chemosensory receptors. Other genes are named using the associated
  19. # AAEL number followed by the name of the Drosophila ortholog (*where orthologs include multiple
  20. # mosquito genes that have the same best match in Drosophila). For example, "AAEL019818" was changed
  21. # to "AAEL019818-nompC". See Methods
  23. DimPlot(MyOrcoMinusOSNs, pt.size=0.5, label=T) # Fig. 3A
  24. DimPlot(MyOrcoMinusOSNs, group.by="orig.ident", pt.size=0.5)
  25. FeaturePlot(MyOrcoMinusOSNs, feature="Ir25a", pt.size=0.5, cols=rev(viridis(100, option='D')), order=T)
  26. FeaturePlot(MyOrcoMinusOSNs, feature="Ir8a", pt.size=0.5, cols=rev(viridis(100, option='D')), order=T)
  27. FeaturePlot(MyOrcoMinusOSNs, feature="Ir76b", pt.size=0.5, cols=rev(viridis(100, option='D')), order=T)
  28. FeaturePlot(MyOrcoMinusOSNs, feature="Ir64a", pt.size=0.5, cols=rev(viridis(100, option='D')), order=T)
  29. genes <- rownames(MyOrcoMinusOSNs)
  30. FeaturePlot(MyOrcoMinusOSNs, features = genes[grep('Amt',genes)], pt.size=0.6, cols=rev(viridis(100, option='D')), order=T) #UMAP for gene of interest with "XXX" somewhere in name
  33. # Fig 3B and S12A-B: Choosing and using log2FC' cutoff to call receptor expression
  35. #------- OPTIONAL (used in Fig. 3B): Merging 4/9 and 14a/14b due to expression of same/similar complement of receptors
  36. MyOrcoMinusOSNs<-RenameIdents(MyOrcoMinusOSNs,"4"="4_9", "9"="4_9", "14a"="14a_14b", "14b"="14a_14b")
  37. clustorder=c("0","7","13","2","10a","10b","1","17","6","12","15","3","16","11","5","4_9","14a_14b","8")
  39. #-------- Assembling list of tuning receptors detected in at least one droplet
  40. receptorlist = rownames(MyOrcoMinusOSNs$SCT@data)[grep('^[OIG]r',rownames(MyOrcoMinusOSNs$SCT@data))] # Add all expressed ORs, GRs, IRs to list
  41. receptorlist <- c(receptorlist, rownames(MyOrcoMinusOSNs$SCT@data)[grep('Amt',rownames(MyOrcoMinusOSNs$SCT@data))]) # Add Amt to list
  42. receptorlist <- receptorlist[!rowSums(MyOrcoMinusOSNs$SCT@data[receptorlist,])==0] # Remove receptors that were not detected in any orco- OSN droplet
  44. #-------- Calculating Log2FC' for each receptor gene in each cluster
  45. MyMarkers <- FindAllMarkers(MyOrcoMinusOSNs, features=receptorlist, logfc.threshold=0, return.thresh=1, min.pct=0, test.use='t')
  46. log2FC <- matrix(NA, nrow=length(receptorlist), ncol=length(levels(MyMarkers$cluster))) # create empty matrix
  47. colnames(log2FC) <- levels(MyMarkers$cluster) # columns are clusters
  48. rownames(log2FC) <- receptorlist # rows are receptors
  49. for(i in 1:nrow(MyMarkers)){ # populate matrix with ave_log2FC values
  50. log2FC[MyMarkers$gene[i],as.character(MyMarkers$cluster[i])]<-MyMarkers$avg_log2FC[i]
  51. }
  52. log2FC[is.na(log2FC)] <- 0 #replacing NAs with 0s
  53. med.att <- apply(log2FC, 1, median) #calculating median Log2FC values (for each gene across clusters)
  54. log2FCprime <- sweep(log2FC, 1, med.att) #subtract median Log2FC value from cluster-specific Log2FCs to get Log2FC'
  56. #---------- Fig. S12A: Plotting log2FC` Histogram (includes values for each Ir/Gr/Or in each cluster)
  57. par(mfrow=c(2,1))
  58. hist(log2FCprime, xlim=c(-2,7), breaks=seq(-2,7,0.1))
  59. abline(v=0.4, col='red') # add threshold value used for calling expression (see below)
  60. hist(log2FCprime, xlim=c(-2,7), ylim=c(0,20), breaks=seq(-2,7,0.1))
  61. abline(v=0.4, col='red') # add threshold value used for calling expression (see below)
  63. #---------- Fig. 3B and S12B: Plotting heatmap of log2FC` values (with dots marking those expressed above cutoff of 0.4)
  64. cutoffthresh = 0.4
  65. displaythresh = cutoffthresh
  66. displaythresh = 0.15 # used for Fig. S12B-C
  67. abovedisplaythresh <- rownames(log2FCprime)[apply(log2FCprime,1,max)>displaythresh]
  68. abovedisplaythresh <- abovedisplaythresh[!(abovedisplaythresh %in% c("Orco","Ir25a","Ir76b","Ir8a","Ir93a"))] # exclude co-receptors from log2FC' heatmap
  70. log2FCP_Heatmap_tuning <-log2FCprime[abovedisplaythresh,clustorder]
  71. receptororder <- abovedisplaythresh[order(apply(log2FCP_Heatmap_tuning,1,which.max))] # order receptors by maximal expression in each sequential cluster
  72. receptororder <- receptororder[c(grep('^[O]r',receptororder),grep('^[G]r',receptororder),grep('^[I]r',receptororder))] # pull Grs and Irs to front
  73. log2FCP_Heatmap_tuning <-log2FCP_Heatmap_tuning[receptororder,]
  74. log2FCP_Heatmap_tuning[log2FCP_Heatmap_tuning<0]<-0 #converting negative log2FC` values to zero
  75. log2FCP_Heatmap <- log2FCP_Heatmap_tuning
  76. color_palette=c(colorRampPalette(c(alpha("white", alpha = 0),'#FDE333'))(10),hcl.colors(450, palette = 'viridis', alpha = NULL, rev = T, fixup = TRUE))
  77. par(mfrow=c(1,1))
  78. image(t(log2FCP_Heatmap),col=color_palette,axes=F,zlim=c(0,round(max(log2FCP_Heatmap))), main='Log2FC`')
  79. axis(3,at=seq(0,1,length=ncol(log2FCP_Heatmap)),las=2,labels=colnames(log2FCP_Heatmap),cex.axis=0.5,lwd=0)
  80. axis(2,at=seq(0,1,length=nrow(log2FCP_Heatmap)),las=2,labels=rownames(log2FCP_Heatmap),cex.axis=0.5,lwd=0)
  81. color.legend(0.05,-0.15,0.2,-0.17,rect.col=color_palette,legend=c(0,round(max(log2FCP_Heatmap))),align='rb')
  82. for(i in 1:length(clustorder)){
  83. for(j in 1:length(receptororder)){
  84. if(log2FCP_Heatmap[receptororder[j],clustorder[i]]>cutoffthresh) text(seq(0,1,length=length(clustorder))[i],
  85. seq(0,1,length=nrow(log2FCP_Heatmap))[j],labels='.',cex=1, col = "black")
  86. }
  87. }
  89. # Fig S12C: Visualizing alternative log average expression cutoff for receptors
  91. AveExp <- AverageExpression(MyOrcoMinusOSNs, features=receptorlist, return.seurat=T)
  92. AveExp_tuning <- AveExp$SCT@data[receptororder,clustorder] # extracting log normalized average counts for same tuning receptors that met the log2FC display threshold used above
  93. AveExp_co <- AveExp$SCT@data[c("Ir25a","Ir76b","Ir8a"),clustorder] # extracting log normalized average counts for co-receptors
  95. #---------- Explore histogram of log average expression values for each receptor in each cluster to choose cutoff
  96. par(mfrow=c(2,1))
  97. hist(AveExp_tuning, breaks=seq(0,5,0.01), main='logAveExpression')
  98. hist(AveExp_tuning, ylim=c(0,50), breaks=seq(0,5,0.01), xlim=c(0,1),main='logAveExpression')
  99. abline(v=0.15)
  101. #---------- Plotting heatmap of log average expression values (including co-receptors)
  102. AveExpcutoffthresh = 0.15
  103. AveExp_Heatmap <- rbind(AveExp_co,AveExp_tuning)
  104. Corcols=hcl.colors(450, palette = 'rocket', alpha = NULL, rev = T, fixup = TRUE)
  105. par(mfrow=c(1,1))
  106. image(t(AveExp_Heatmap),col=Corcols,axes=F, zlim=c(0,ceiling(max(AveExp_Heatmap))), main='Log average expression')
  107. axis(3,at=seq(0,1,length=ncol(AveExp_Heatmap)),las=2,labels=colnames(AveExp_Heatmap),cex.axis=0.5,lwd=0)
  108. axis(2,at=seq(0,1,length=nrow(AveExp_Heatmap)),las=2,labels=rownames(AveExp_Heatmap),cex.axis=0.3,lwd=0)
  109. color.legend(0.05,-0.15,0.2,-0.17,rect.col=Corcols,legend=c(0,ceiling(max(AveExp_Heatmap))),align='rb')
  110. # add dots for receptors that exceed log ave expression threshold
  111. for(i in 1:length(clustorder)){
  112. for(j in 1:length(receptororder)){
  113. y<-j+3 # accounts for space taken up by co-receptors at bottom of plot
  114. if(AveExp_Heatmap[receptororder[j],clustorder[i]]>AveExpcutoffthresh) text(seq(0,1,length=length(clustorder))[i],
  115. seq(0,1,length=nrow(AveExp_Heatmap))[y],labels='.',cex=1, col = "black")
  116. }
  117. }
  120. # Fig. S12D-E: Calculating and plotting pairwise Pearson's correlations
  122. #---------- Refresh list of receptors with log2FC'>0.15 in at least one cluster and get count data for all droplets
  123. myreceptors <- rownames(log2FCprime)[apply(log2FCprime,1,max)>displaythresh]
  124. myreceptors <- myreceptors[!(myreceptors %in% c("Orco","Ir25a","Ir76b","Ir8a","Ir93a"))]
  125. MyData <- subset(MyOrcoMinusOSNs,features=myreceptors)
  126. MyMatrix <- t(as.matrix(MyData@assays$SCT@data))
  128. #---------- Generate matrix of pairwise correlations and reorder the receptors
  129. log2FCP_matrix <- log2FCprime[myreceptors,clustorder]
  130. receptororder <- myreceptors[order(apply(log2FCP_matrix,1,which.max))] # order receptors by maximal expression in each sequential cluster
  131. receptororder <- receptororder[c(grep('^[G]r',receptororder),grep('^[I]r',receptororder))] # pull Grs and Irs to front
  133. cormat <- cor(MyMatrix, method = "pearson", use = 'everything')
  134. cormat <- cormat[receptororder,receptororder] # reorder receptors
  136. #---------- Generate matrix showing which genes are called as coexpressed in at least one cluster
  137. coexp = matrix(data=F, nrow=length(receptororder), ncol=length(receptororder))
  138. colnames(coexp) <- receptororder
  139. rownames(coexp) <- receptororder
  140. for (i in 1:nrow(coexp)) {
  141. for (j in 1:ncol(coexp)) {
  142. is_coexp = F
  143. for (k in 1:ncol(log2FCP_Heatmap)) { # check whether the two receptors BOTH have log2FC'>0.3 in any of k clusters
  144. if (sum(c(log2FCP_Heatmap[receptororder[i],k]>cutoffthresh,log2FCP_Heatmap[receptororder[j],k]>cutoffthresh))==2) { is_coexp = T }
  145. }
  146. if (is_coexp) { coexp[i,j] = T }
  147. }
  148. }
  150. #---------- Fig. S12E: Plot heatmap of pairwise correlations for all receptors with log2FC'>0.15 in at least one cluster
  151. par(mfrow=c(1,1))
  152. image(cormat,col=viridis(256, direction = -1, option = "B"),zlim=c(0,1),axes=F)
  153. axis(1,at=seq(0,1,length=nrow(cormat)),las=2,labels=rownames(cormat),cex.axis=0.5,lwd=0)
  154. axis(2,at=seq(0,1,length=ncol(cormat)),las=2,labels=colnames(cormat),cex.axis=0.5,lwd=0)
  155. color.legend(0.01,0.99,0.1,1.0,rect.col=viridis(256, direction = -1, option = "B"),legend=c(0,1),align='rb')
  156. # Add dots for squares that correspond to receptors called as co-expressed
  157. for(i in 1:length(receptororder)){
  158. for(j in 1:length(receptororder)){
  159. if(coexp[receptororder[i],receptororder[j]]) text(seq(0,1,length=length(receptororder))[i],
  160. seq(0,1,length=length(receptororder))[j],labels='.',cex=1, col = "black")
  161. }
  162. }
  164. #---------- Limit matrices to 'expressed' IRs (with log2FC'>0.4 in at least one cluster)
  165. myexpressedIRs <- rownames(log2FCprime)[apply(log2FCprime,1,max)>cutoffthresh]
  166. myexpressedIRs <- myexpressedIRs[!(myexpressedIRs %in% c("Orco","Ir25a","Ir76b","Ir8a","Ir93a"))]
  167. myexpressedIRs <- myexpressedIRs[grep('^[I]r',myexpressedIRs)] #limit to IRs only
  168. cormat2 <- cormat
  169. cormat2 <- cormat2[rownames(cormat2) %in% myexpressedIRs,colnames(cormat2) %in% myexpressedIRs]
  170. coexp2 <- coexp
  171. coexp2 <- coexp2[rownames(coexp2) %in% myexpressedIRs,colnames(coexp2) %in% myexpressedIRs]
  173. #---------- Fig. S12D: Plotting histogram of pairwise Pearson correlations for 'expressed' IRs
  174. YEScoexpressed <- cormat2[upper.tri(cormat2, diag=F)][coexp2[upper.tri(cormat2, diag=F)]]
  175. NOcoexpressed <- cormat2[upper.tri(cormat2, diag=F)][!coexp2[upper.tri(cormat2, diag=F)]]
  176. par(mfrow=c(2,1))
  177. hist(NOcoexpressed, xlim=c(-0.2,0.9), breaks=seq(-0.2,0.9,0.02))
  178. hist(YEScoexpressed, xlim=c(-0.2,0.9), breaks=seq(-0.2,0.9,0.02))
  181. # Fig. S10: Dotplots of marker expression
  183. MyOrcoMinusOSNs_Marks<-FindAllMarkers(MyOrcoMinusOSNs,logfc.threshold=0.3, return.thresh=1,min.pct=0,test.use='t', only.pos = T)
  184. MyOrcoMinusOSNs_markers <- Extract_Top_Markers(marker_dataframe = MyOrcoMinusOSNs_Marks, num_genes = 900, named_vector = FALSE,
  185. make_unique = TRUE)
  186. Clustered_DotPlot(seurat_object = MyOrcoMinusOSNs, features = MyOrcoMinusOSNs_markers, exp_color_min=0, exp_color_max = 2,
  187. colors_use_exp = viridis(50, direction = -1, option = "D"), plot_km_elbow=F, raster = T)
  189. # Fig. 5A: Plot phylogenetic VS genome distance for pairs of receptors
  191. IRcoexp <- coexp2 # use geneset that includes all IRs with log2FC'>0.4
  193. #---------- Load genomic distances
  194. # Use the following linux code to extract OR and IR start positions from the updated annotation file
  195. # awk '$3 == "transcript" && $12 ~ /^\"[OI]r/ {split($12, a, "\""); print $1 "\t" $4 "\t" a[2]}' AaegyptiLVP_AGWG_ThreePrimeUTRextended_Adavi2024.gtf > ORIRpositions.txt
  196. # Navigate to directory containing the new file
  197. genomicPositions <- read.table("ORIRpositions.txt", header=T)
  198. rownames(genomicPositions) <- genomicPositions$Gene
  199. calculate_distance <- function(gene1, gene2) {
  200. if (gene1$Chr == gene2$Chr) {distance <- abs(gene1$Start - gene2$Start)} #if genes on same chromosome
  201. else {distance <- 600000000} #or else just return very large number
  202. return(distance)
  203. }
  205. #---------- Load pairwise phylogenetic distances from treefile
  206. library(ggtree)
  207. library(ape)
  208. # Navigate to directory containing the new file
  209. tree_Aedes_irs <- read.tree(file = "Aedes_IR_tree.txt") # Load IR tree
  210. tree_Aedes_irs <- root(tree_Aedes_irs,outgroup='Ir25a') # Re-root tree
  211. IRpairwise_phylo <- cophenetic.phylo(tree_Aedes_irs)
  213. #---------- Populate matrices
  214. IRgenodist <- matrix(nrow=nrow(IRcoexp), ncol=ncol(IRcoexp))
  215. rownames(IRgenodist) <- rownames(IRcoexp)
  216. colnames(IRgenodist) <- colnames(IRcoexp)
  217. IRphylodist <- IRgenodist
  218. IRcoexpcol <- matrix("black",nrow=nrow(IRcoexp), ncol=ncol(IRcoexp))
  219. IRcoexpcol[IRcoexp] <- "green"
  220. for (i in (1:nrow(IRcoexp))) {
  221. for (j in (1:ncol(IRcoexp))) {
  222. gene1 <- rownames(IRcoexp)[i]
  223. gene2 <- colnames(IRcoexp)[j]
  224. IRgenodist[i,j] <- calculate_distance(genomicPositions[gene1,],genomicPositions[gene2,])
  225. IRphylodist[i,j] <- IRpairwise_phylo[gene1,gene2]
  226. }
  227. }
  229. #---------- Fig. 5A: Plot genomic by phylogenetic distances (colored by whether genes are coexpressed)
  230. par(mfrow=c(2,1))
  231. MyGenodists = log10(IRgenodist[upper.tri(IRcoexp)])
  232. for (i in 1:length(MyGenodists)) { if (MyGenodists[i]==log10(600000000)) MyGenodists[i]=jitter(9,factor=0.3) }
  233. plot(MyGenodists,IRphylodist[upper.tri(IRcoexp)],xlab="Genomic distance (log10(bp))",ylab="Phylogenetic distance",col=IRcoexpcol[upper.tri(IRcoexp)])
  234. plot(IRgenodist[upper.tri(IRcoexp)],IRphylodist[upper.tri(IRcoexp)],xlab="Genomic distance (bp)",ylab="Phylogenetic distance",col=IRcoexpcol[upper.tri(IRcoexp)])
  236. #---------- Fig. 5A: Plot marginal densities for phylogenetic distances (separately for genes that are or are not coexpressed)
  237. par(mfrow=c(2,1))
  238. plot(density(IRphylodist[upper.tri(IRcoexp) & !IRcoexp]),xlim=c(0,0.14))
  239. plot(density(IRphylodist[upper.tri(IRcoexp) & IRcoexp]),xlim=c(0,0.14),col="green")

orcoMinusOSN_analyses_v8.R at commit ff813a7, no license · at the source

Overview

Authors: Elisha David1,2,3,4, Vitor L. dos Anjos5, Sumer M. Kotb5,6, Melanie Edwards5,7, Hillery C. Metz5,8, David Tian5,9, Zhilei Zhao1,5,10, Jessica L. Zung1,5,11, Noah H. Rose5,12, Carolyn S. McBride1,2,5
ORCID iDs: Elisha David, Vitor L. dos Anjos, Sumer M. Kotb, Melanie Edwards, David Tian, Jessica L. Zung, Noah H. Rose, Carolyn S. McBride
  1. Princeton Neuroscience Institute, Princeton University,Princeton, NJ USA
  2. Department of Molecular Biology, Princeton University,Princeton, NJ USA
  3. Present Address: EDEN Medical Genomics, Jerusalem, Israel
  4. Present Address: Leumit Health Services, Tel-Aviv, Israel
  5. Department of Ecology and Evolutionary Biology, Princeton University,Princeton, NJ USA
  6. Present Address: Biologics Process Research and Development, Merck & Co,Rahway, NJ USA
  7. Present Address: Muséum national d’Histoire naturelle,Paris, France
  8. Present Address: Medical and Scientific Affairs, Illumina Inc,San Diego, CA USA
  9. Present Address: Department of Ecology and Evolutionary Biology, University of California,Los Angeles, CA USA
  10. Present Address: State Key Laboratory of Animal Biodiversity and Conservation and Integrated Pest Management, Institute of Zoology, Chinese Academy of Sciences,Beijing, China
  11. Present Address: Zuckerman Mind Brain Behavior Institute, Columbia University,New York, NY USA
  12. Present Address: Department of Ecology, Behavior, and Evolution, University of California,San Diego, CA USA
Institutions: Princeton University (United States); Merck & Co., Inc., Rahway, NJ, USA (United States) (United States); Muséum national d'Histoire naturelle (France); Illumina (United States) (United States); University of California, Los Angeles (United States); Chinese Academy of Sciences (China); Columbia University (United States); University of California San Diego (United States)
Journal: Nature communications, volume 17, issue 1, article 9828
Dates: received 7 April 2026; accepted 4 August 2026; published online 15 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-76682-2 · PMID 42736291 · PMCID PMC13575207 · OpenAlex W7203546563
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other (organism), drosophila (organism), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Evoked potentials, Connectivity, Machine learning
Keywords: Olfactory receptors, Molecular neuroscience, Genetics of the nervous system
MeSH: Aedes*, Insect Proteins*, Olfactory Receptor Neurons*, Receptors, Odorant*, Animals, Dengue, Drosophila, Drosophila melanogaster, Female, Gene Expression Regulation, Transcriptome (* major topic)
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Allergy and Infectious Diseases (R01AI175490); New York Stem Cell Foundation (NYSCF) (Robertson Neuroscience Investigator Award)
Citations: cited by 4 papers (Europe PMC); 71 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

mcbridelab/Adavi_2024_snRNAseqAaegAntennae

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ff813a7183ae56cbf3a03a5458450fdf057e674e, 16 August 2024
Languages: R (4), Python (1)
Size: 9 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (4 files), ggpubr (1 file), NumPy (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/mcbridelab/Adavi_2024_snRNAseqAaegAntennae

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-76682-2.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • 16 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-76682-2.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 11 MeSH terms, 2 funders, 67 references.

Cite

This paper

David, E., dos Anjos, V. L., Kotb, S. M., Edwards, M., Metz, H. C., Tian, D., Zhao, Z., Zung, J. L., Rose, N. H., & McBride, C. S. (2026). Odorant receptor coexpression and multi-expression in the dengue mosquito. Nature communications, 17(1), 9828. https://doi.org/10.1038/s41467-026-76682-2

BibTeX

@article{david2026odorant,
author = {David, Elisha and dos Anjos, Vitor L. and Kotb, Sumer M. and Edwards, Melanie and Metz, Hillery C. and Tian, David and Zhao, Zhilei and Zung, Jessica L. and Rose, Noah H. and McBride, Carolyn S.},
title = {{Odorant receptor coexpression and multi-expression in the dengue mosquito}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9828},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76682-2},
url = {https://doi.org/10.1038/s41467-026-76682-2},
pmid = {42736291},
pmcid = {PMC13575207}
}

RIS

TY - JOUR
AU - David, Elisha
AU - dos Anjos, Vitor L.
AU - Kotb, Sumer M.
AU - Edwards, Melanie
AU - Metz, Hillery C.
AU - Tian, David
AU - Zhao, Zhilei
AU - Zung, Jessica L.
AU - Rose, Noah H.
AU - McBride, Carolyn S.
TI - Odorant receptor coexpression and multi-expression in the dengue mosquito
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/15
VL - 17
IS - 1
SP - 9828
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76682-2
UR - https://doi.org/10.1038/s41467-026-76682-2
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
"author": [
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"family": "David",
"given": "Elisha"
},
{
"family": "dos Anjos",
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{
"family": "Kotb",
"given": "Sumer M."
},
{
"family": "Edwards",
"given": "Melanie"
},
{
"family": "Metz",
"given": "Hillery C."
},
{
"family": "Tian",
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{
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"given": "Zhilei"
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{
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"given": "Jessica L."
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{
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"given": "Noah H."
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"given": "Carolyn S."
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"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9828",
"DOI": "10.1038/s41467-026-76682-2",
"PMID": "42736291",
"PMCID": "PMC13575207",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-76682-2",
"language": "en",
"issued": {
"date-parts": [
[
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15
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]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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