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Altered Retinal Dopamine Homeostasis in Murine Models of Retinitis Pigmentosa.

Code ↔ Paper

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  1. [1] § Results › RNA Sequencing Indicates Widespread Alterations in the Catecholaminergic System in P23H Retinas ↔ NormalizeDataDEandGOanalysis.R, lines 253–337 · score 0.62 · KEGG pathway, RNA seq, fold change, regulatory, log2, genes
  2. [2] § Materials and Methods › Bulk RNA Sequencing ↔ NormalizeDataDEandGOanalysis.R, lines 253–337 · score 0.53 · log2 fold change, DESeq2, Genes, RNA

Paper

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The authors' code

R · 337 lines · 13 KB · no license · 2 matches

  1. # Install the latest version of DEseq2
  2. if (!requireNamespace("BiocManager", quietly = TRUE))
  3. install.packages("BiocManager")
  4. BiocManager::install("DESeq2")
  6. # load the library
  7. library(DESeq2)
  9. setwd("/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles")
  12. # Read in the raw read counts
  13. rawCounts <- read.delim("ngs-data-table_countFile.tsv")
  14. head(rawCounts)
  16. # Read in the sample mappings
  17. sampleData <- read.delim("phenodata.tsv")
  18. head(sampleData)
  20. # Also save a copy for later
  21. sampleData_v2 <- sampleData
  23. # Convert count data to a matrix of appropriate form that DESeq2 can read
  24. geneName <- rawCounts$GeneName
  25. sampleIndex <- grepl("ERR\\d+", colnames(rawCounts))
  26. rawCounts <- as.matrix(rawCounts[,sampleIndex])
  27. rownames(rawCounts) <- geneName
  28. head(rawCounts)
  30. write.csv(rawCounts,"rawCounts.csv",sep = "\t", row.names = TRUE)
  32. # Convert sample variable mappings to an appropriate form that DESeq2 can read
  33. head(sampleData)
  34. rownames(sampleData) <- sampleData$sample
  35. keep <- c("group_details", "group")
  36. sampleData <- sampleData[,keep]
  37. colnames(sampleData) <- c("group_details", "group")
  38. sampleData$group <- factor(sampleData$group)
  39. head(sampleData)
  41. # Put the columns of the count data in the same order as rows names of the sample mapping, then make sure it worked
  42. rawCounts <- rawCounts[,unique(rownames(sampleData))]
  43. all(colnames(rawCounts) == rownames(sampleData))
  45. # Order the tissue types so that it is sensible and make sure the control sample is first: normal sample -> primary tumor -> metastatic tumor
  46. sampleData$group_details <- factor(sampleData$group_details, levels=c("Diseased", "DrugTreated"))
  48. # Create the DESeq2DataSet object
  49. deseq2Data <- DESeqDataSetFromMatrix(countData=rawCounts, colData=sampleData, design= ~ group)
  51. dim(deseq2Data)
  52. dim(deseq2Data[rowSums(counts(deseq2Data)) > 5, ])
  54. # Perform pre-filtering of the data
  55. deseq2Data <- deseq2Data[rowSums(counts(deseq2Data)) > 5, ]
  57. # Run pipeline for differential expression
  58. deseq2Data <- DESeq(deseq2Data)
  60. # Extract differential expression results
  61. deseq2Results <- results(deseq2Data, contrast=c("group", "1", "2"))
  63. # View summary of results
  64. summary(deseq2Results)
  66. # Using DEseq2 built in method
  67. plotMA(deseq2Results)
  69. # Load libraries for ggplot
  70. library(ggplot2)
  71. library(scales)
  72. library(viridis)
  74. # Coerce to a data frame
  75. deseq2ResDF <- as.data.frame(deseq2Results)
  77. # Examine this data frame
  78. head(deseq2ResDF)
  80. # Set a boolean column for significance
  81. deseq2ResDF$significant <- ifelse(deseq2ResDF$padj < .1, "Significant", NA)
  83. # Plot the results similar to DEseq2
  84. ggplot(deseq2ResDF, aes(baseMean, log2FoldChange, colour=significant)) +
  85. geom_point(size=1) +
  86. scale_y_continuous(limits=c(-3, 3), oob=squish) +
  87. scale_x_log10() +
  88. geom_hline(yintercept = 0, colour="tomato1", size=2) +
  89. labs(x="mean of normalized counts", y="log fold change") +
  90. scale_colour_manual(name="q-value", values=("Significant"="red"), na.value="grey50") +
  91. theme_bw()
  93. # Variance stabilizing transformation
  94. deseq2VST <- vst(deseq2Data)
  96. # Convert the DESeq transformed object to a data frame
  97. deseq2VST <- assay(deseq2VST)
  98. deseq2VST <- as.data.frame(deseq2VST)
  99. deseq2VST$Gene <- rownames(deseq2VST)
  100. head(deseq2VST)
  102. write.csv(deseq2VST,"/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/deseq2VST.csv", row.names = TRUE) # Save after variance stabilizing transform
  104. # Keep only the significantly differentiated genes where the fold-change was at least 3
  105. sigGenes <- rownames(deseq2ResDF[deseq2ResDF$padj <= .05 & abs(deseq2ResDF$log2FoldChange) > 3,])
  106. deseq2VST <- deseq2VST[deseq2VST$Gene %in% sigGenes,]
  108. head(deseq2VST)
  110. # Convert the VST counts to long format for ggplot2
  111. library(reshape2)
  113. deseq2VST_long <- melt(deseq2VST, id.vars=c("Gene"))
  115. # Plot the heatmap
  116. heatmap <- ggplot(deseq2VST_long, aes(x=variable, y=Gene, fill=value)) +
  117. geom_raster() +
  118. scale_fill_viridis(trans="sqrt") +
  119. theme(axis.text.x=element_text(angle=65, hjust=1), axis.text.y=element_blank(), axis.ticks.y=element_blank())
  120. heatmap
  122. # Save the results
  123. write.csv(deseq2ResDF,"/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/deseq2ResDF.csv", row.names = TRUE)
  126. library(reshape2)
  128. # First compare wide vs long version
  129. deseq2VST_wide <- deseq2VST
  130. deseq2VST_long <- melt(deseq2VST, id.vars=c("Gene"))
  132. head(deseq2VST_wide)
  133. head(deseq2VST_long)
  135. # Now overwrite our original data frame with the long format
  136. deseq2VST <- melt(deseq2VST, id.vars=c("Gene"))
  138. # Make a heatmap
  139. heatmap <- ggplot(deseq2VST, aes(x=variable, y=Gene, fill=value)) + geom_raster() + scale_fill_viridis(trans="sqrt") + theme(axis.text.x=element_text(angle=65, hjust=1), axis.text.y=element_blank(), axis.ticks.y=element_blank())
  140. heatmap
  143. # Convert the significant genes back to a matrix for clustering
  144. deseq2VSTMatrix <- dcast(deseq2VST, Gene ~ variable)
  145. rownames(deseq2VSTMatrix) <- deseq2VSTMatrix$Gene
  146. deseq2VSTMatrix$Gene <- NULL
  148. # Compute a distance calculation on both dimensions of the matrix
  149. distanceGene <- dist(deseq2VSTMatrix)
  150. distanceSample <- dist(t(deseq2VSTMatrix))
  152. # Cluster based on the distance calculations
  153. clusterGene <- hclust(distanceGene, method="average")
  154. clusterSample <- hclust(distanceSample, method="average")
  156. # Construct a dendogram for samples
  157. #install.packages("ggdendro")
  158. library(ggdendro)
  159. sampleModel <- as.dendrogram(clusterSample)
  160. sampleDendrogramData <- segment(dendro_data(sampleModel, type = "rectangle"))
  161. sampleDendrogram <- ggplot(sampleDendrogramData) + geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) + theme_dendro()
  163. # Re-factor samples for ggplot2
  164. deseq2VST$variable <- factor(deseq2VST$variable, levels=clusterSample$labels[clusterSample$order])
  166. # Construct the heatmap. note that at this point we have only clustered the samples NOT the genes
  167. heatmap <- ggplot(deseq2VST, aes(x=variable, y=Gene, fill=value)) + geom_raster() + scale_fill_viridis(trans="sqrt") + theme(axis.text.x=element_text(angle=65, hjust=1), axis.text.y=element_blank(), axis.ticks.y=element_blank())
  168. heatmap
  170. # Combine the dendrogram and the heatmap
  171. #install.packages("gridExtra")
  172. library(gridExtra)
  173. grid.arrange(sampleDendrogram, heatmap, ncol=1, heights=c(1,5))
  176. # Load in libraries necessary for modifying plots
  177. #install.packages("gtable")
  178. library(gtable)
  179. library(grid)
  181. # Modify the ggplot objects
  182. sampleDendrogram_1 <- sampleDendrogram + scale_x_continuous(expand=c(.0085, .0085)) + scale_y_continuous(expand=c(0, 0))
  183. heatmap_1 <- heatmap + scale_x_discrete(expand=c(0, 0)) + scale_y_discrete(expand=c(0, 0))
  185. # Convert both grid based objects to grobs
  186. sampleDendrogramGrob <- ggplotGrob(sampleDendrogram_1)
  187. heatmapGrob <- ggplotGrob(heatmap_1)
  189. # Check the widths of each grob
  190. sampleDendrogramGrob$widths
  191. heatmapGrob$widths
  193. # Add in the missing columns
  194. sampleDendrogramGrob <- gtable_add_cols(sampleDendrogramGrob, heatmapGrob$widths[7], 6)
  195. sampleDendrogramGrob <- gtable_add_cols(sampleDendrogramGrob, heatmapGrob$widths[8], 7)
  197. # Make sure every width between the two grobs is the same
  198. maxWidth <- unit.pmax(sampleDendrogramGrob$widths, heatmapGrob$widths)
  199. sampleDendrogramGrob$widths <- as.list(maxWidth)
  200. heatmapGrob$widths <- as.list(maxWidth)
  202. # Arrange the grobs into a plot
  203. finalGrob <- arrangeGrob(sampleDendrogramGrob, heatmapGrob, ncol=1, heights=c(2,5))
  205. # Draw the plot
  206. grid.draw(finalGrob)
  208. # Re-order the sample data to match the clustering we did
  209. sampleData_v2$Run <- factor(sampleData_v2$sample, levels=clusterSample$labels[clusterSample$order])
  211. # Construct a plot to show the clinical data
  212. colours <- c("#743B8B", "#8B743B")
  213. sampleClinical <- ggplot(sampleData_v2, aes(x=Run, y=1, fill=group_details)) + geom_tile() + scale_x_discrete(expand=c(0, 0)) + scale_y_discrete(expand=c(0, 0)) + scale_fill_manual(name="Tissue", values=colours) + theme_void()
  215. # Convert the clinical plot to a grob
  216. sampleClinicalGrob <- ggplotGrob(sampleClinical)
  218. # Make sure every width between all grobs is the same
  219. maxWidth <- unit.pmax(sampleDendrogramGrob$widths, heatmapGrob$widths, sampleClinicalGrob$widths)
  220. sampleDendrogramGrob$widths <- as.list(maxWidth)
  221. heatmapGrob$widths <- as.list(maxWidth)
  222. sampleClinicalGrob$widths <- as.list(maxWidth)
  224. # Arrange and output the final plot
  225. finalGrob <- arrangeGrob(sampleDendrogramGrob, sampleClinicalGrob, heatmapGrob, ncol=1, heights=c(2,1,5))
  226. grid.draw(finalGrob)
  228. write.csv(deseq2ResDF,"deseq2ResDF.csv", row.names = TRUE)
  245. ############################### GSEA ##########################
  247. # Install necessary packages if not already installed
  248. if (!requireNamespace("BiocManager", quietly = TRUE))
  249. install.packages("BiocManager")
  250. BiocManager::install(c("gage", "GO.db", "AnnotationDbi", "org.Hs.eg.db"))
  251. library(gage)
  253. # Conduct DE analysis for female vs male comparison
  254. female_v_male_DE <- results(deseq2Data, contrast=c("group", "1", "2"))
  256. # Set up KEGG database for human
  257. kg.hs <- kegg.gsets(species="hsa")
  258. kegg.sigmet.gs <- kg.hs$kg.sets[kg.hs$sigmet.idx]
  259. kegg.dise.gs <- kg.hs$kg.sets[kg.hs$dise.idx]
  261. # Set up GO database for human
  262. go.hs <- go.gsets(species="Human")
  263. go.bp.gs <- go.hs$go.sets[go.hs$go.subs$BP]
  264. go.mf.gs <- go.hs$go.sets[go.hs$go.subs$MF]
  265. go.cc.gs <- go.hs$go.sets[go.hs$go.subs$CC]
  267. # Load required annotation packages
  268. library(AnnotationDbi)
  269. library(org.Hs.eg.db)
  270. library(biomaRt)
  272. # Annotate DESeq2 results with additional gene identifiers (ENSEMBL, ENTREZ, GeneName)
  273. female_v_male_DE$ENSEMBL <- mapIds(org.Hs.eg.db, keys=row.names(female_v_male_DE), column="ENSEMBL", keytype="SYMBOL", multiVals="first")
  274. female_v_male_DE$entrez <- mapIds(org.Hs.eg.db, keys=row.names(female_v_male_DE), column="ENTREZID", keytype="SYMBOL", multiVals="first")
  275. female_v_male_DE$name <- mapIds(org.Hs.eg.db, keys=row.names(female_v_male_DE), column="GENENAME", keytype="SYMBOL", multiVals="first")
  277. # Grab the log2 fold changes for all genes
  278. female_v_male_DE.fc <- female_v_male_DE$log2FoldChange
  279. names(female_v_male_DE.fc) <- female_v_male_DE$entrez
  281. # Run GAGE enrichment analysis for all log2 fold changes
  282. fc.kegg.sigmet.p <- gage(female_v_male_DE.fc, gsets = kegg.sigmet.gs)
  283. fc.kegg.dise.p <- gage(female_v_male_DE.fc, gsets = kegg.dise.gs)
  284. fc.go.bp.p <- gage(female_v_male_DE.fc, gsets = go.bp.gs)
  285. fc.go.mf.p <- gage(female_v_male_DE.fc, gsets = go.mf.gs)
  286. fc.go.cc.p <- gage(female_v_male_DE.fc, gsets = go.cc.gs)
  288. # Convert the KEGG results to data frames for up-regulated pathways
  289. fc.kegg.sigmet.p.up <- as.data.frame(fc.kegg.sigmet.p$greater)
  290. fc.kegg.dise.p.up <- as.data.frame(fc.kegg.dise.p$greater)
  292. # Write the results to files
  293. write.table(fc.kegg.sigmet.p.up, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.kegg.sigmet.p.up.txt", sep = ";", row.names = TRUE)
  294. write.table(fc.kegg.dise.p.up, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.kegg.dise.p.up.txt", sep = ";", row.names = TRUE)
  296. # Convert the KEGG results for down-regulated pathways
  297. fc.kegg.sigmet.p.down <- as.data.frame(fc.kegg.sigmet.p$less)
  298. fc.kegg.dise.p.down <- as.data.frame(fc.kegg.dise.p$less)
  300. # Write the results for down-regulated pathways
  301. write.table(fc.kegg.sigmet.p.down, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.kegg.sigmet.p.down.txt", sep = ";", row.names = TRUE)
  302. write.table(fc.kegg.dise.p.down, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.kegg.dise.p.down.txt", sep = ";", row.names = TRUE)
  304. # Convert the GO results to data frames for up-regulated terms
  305. fc.go.bp.p.up <- as.data.frame(fc.go.bp.p$greater)
  306. fc.go.mf.p.up <- as.data.frame(fc.go.mf.p$greater)
  307. fc.go.cc.p.up <- as.data.frame(fc.go.cc.p$greater)
  309. # Write the results for GO up-regulated terms
  310. write.table(fc.go.bp.p.up, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.go.bp.p.up.txt", sep = ";", row.names = TRUE)
  311. write.table(fc.go.mf.p.up, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.go.mf.p.up.txt", sep = ";", row.names = TRUE)
  312. write.table(fc.go.cc.p.up, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.go.cc.p.up.txt", sep = ";", row.names = TRUE)
  314. # Convert the GO results for down-regulated terms
  315. fc.go.bp.p.down <- as.data.frame(fc.go.bp.p$less)
  316. fc.go.mf.p.down <- as.data.frame(fc.go.mf.p$less)
  317. fc.go.cc.p.down <- as.data.frame(fc.go.cc.p$less)
  319. # Write the results for GO down-regulated terms
  320. write.table(fc.go.bp.p.down, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.go.bp.p.down.txt", sep = ";", row.names = TRUE)
  321. write.table(fc.go.mf.p.down, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.go.mf.p.down.txt", sep = ";", row.names = TRUE)
  322. write.table(fc.go.cc.p.down, "/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/fc.go.cc.p.down.txt", sep = ";", row.names = TRUE)
  324. # Install pathview for pathway visualization
  325. if (!require("BiocManager", quietly = TRUE))
  326. install.packages("BiocManager")
  328. BiocManager::install("pathview")
  329. setwd("/home/umair/Documents/RNA_Seq/humanData_20250401/below60Data/readcountFiles/GSEA/")
  331. library(pathview)
  333. # Display KEGG pathway example (e.g., mmu04744)
  334. pathview(gene.data=female_v_male_DE.fc, species="hsa", pathway.id="hsa00071")
  336. # Display another KEGG pathway example (e.g., mmu04080)
  337. pathview(gene.data=female_v_male_DE.fc, species="hsa", pathway.id="hsa00071", kegg.native=FALSE)

NormalizeDataDEandGOanalysis.R at commit 77df717, no license · at the source

Overview

Authors: Katri Vainionpää1, Eerik Lappalainen1, Anna Nilsson2, Ida Pavela1, Tommi Torsti1, Umair Seemab1, Elina Mäkinen1, Reza Shariatgorji2, Per E. Andrén2,3, Aaro Jalkanen1, Markus M. Forsberg1, Henri Leinonen1
ORCID iDs: Eerik Lappalainen, Tommi Torsti, Per E. Andrén, Aaro Jalkanen, Markus M. Forsberg, Henri Leinonen
  1. School of Pharmacy, Faculty of Health Sciences University of Eastern Finland Kuopio Finland
  2. Department of Pharmaceutical Biosciences, Spatial Mass Spectrometry, Science for Life Laboratory Uppsala University Uppsala Sweden
  3. Wits Integrated Molecular Physiology Research Initiative, Department of Physiology, Faculty of Health Sciences, School of Biomedical Sciences University of the Witwatersrand Johannesburg South Africa
Institutions: University of Eastern Finland (Finland); Uppsala University (Sweden); Science for Life Laboratory (Sweden); University of the Witwatersrand (South Africa)
Journal: Journal of neurochemistry, volume 170, issue 9, article e70552
Dates: received 1 July 2026; accepted 4 September 2026; published online 18 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/jnc.70552 · PMID 42757827 · PMCID PMC13587835 · OpenAlex W7213559601
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics
MeSH: Dopamine*, Homeostasis*, Retina*, Retinitis Pigmentosa*, Animals, Catechol O-Methyltransferase, Disease Models, Animal, Mice, Mice, Inbred C57BL (* major topic)
Topic: Retinal Development and Disorders (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Research Council of Finland; Jane ja Aatos Erkon Säätiö; Federation of European Biochemical Societies; Science for Life Laboratory
Citations: not cited yet (Europe PMC); 63 references in the paper
Research resources: placed in secondary antibody RRID:AB_2099233, RRID:AB_2716568, RRID:AB_2732856, RRID:AB_2752244, RRID:AB_3677640, RRID:AB_399391, RRID:IMSR_JAX:000664, RRID:IMSR_JAX:004297, Heterozygous P23H rhodopsin‐mutated mice RRID:IMSR_JAX:017628, Results were analysed in GraphPad Prism RRID:SCR_002798, Images were analysed in ImageJ RRID:SCR_003070, RRID:SCR_004463, Acquired data was analysed in SCiLS Lab RRID:SCR_014426, RRID:SCR_014583, RRID:SCR_014966, RRID:SCR_015687, Results were analysed in Microsoft Excel RRID:SCR_016137, RRID:SCR_020240, RRID:SCR_023156, RRID:SCR_023615, RRID:SCR_023732, Blot images were captured with Azure 600 RRID:SCR_023780, RRID:SCR_026146

Abstract

Retinitis pigmentosa (RP) is a heterogeneous group of currently untreatable inherited retinal degenerations that share similar disease manifestations despite distinctive genetic causes. Dopamine (DA) is an important and tightly regulated catecholaminergic neurotransmitter required for multiple modulatory functions throughout the central nervous system, including the retina. Dysregulation of DA homeostasis has been reported during neurodegeneration, but how it is regulated in RP remains poorly understood. Here, we demonstrate that early RP is associated with marked dysregulation of the dopaminergic system using two distinct disease models, P23H and rd10 mice. We found increased DA levels in RP retinas collected from pre‐weaned (P12), juvenile (P30), and adult (P60–90) mice by utilising ultra‐high‐performance liquid chromatography and matrix‐assisted laser desorption/ionisation mass spectrometry. Consistently, DA levels were also elevated in the vitreous of P23H mice, where it likely diffuses from the retina. RP retinas additionally demonstrated higher levels of DA precursor l‐3,4‐dihydroxyphenylalanine (L‐DOPA), as well as upregulated tyrosine hydroxylase gene expression, which suggests elevated DA synthesis. In parallel, we observed increased activity and expression of the catecholamine‐metabolising enzyme catechol‐O‐methyltransferase (COMT) during retinal degeneration. Comparative tissue analysis with cortex, striatum, retina, and eye cup samples further highlighted COMT as a major contributor to catecholamine inactivation in the mouse retina. Finally, RNA sequencing of P23H retinal extracts revealed widespread alterations related to the catecholaminergic system during disease progression, including upregulation of the solute carrier family 6 member 2 gene, encoding norepinephrine transporter, and the protein phosphatase 1 regulatory subunit 1B gene, which encodes DA‐ and cAMP‐regulated neuronal phosphoprotein. In conclusion, these data demonstrate elevated DA levels in P23H and rd10 mouse retinas, along with several other biochemical changes in the catecholamine system, suggesting a hyperexcited state in early RP progression.

Reproduced under the paper's license (CC BY), from the paper cited above.

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UmairSeemab/RNAseqData

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 77df717e34ffb3437b76783a0de141409f2cf060, 3 April 2025
Languages: Shell (2), R (1)
Size: 10 files, 3 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: STAR (2 files), DESeq2 (1 file), ggplot2 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
4 files
At the source: github.com/UmairSeemab/RNAseqData

The paper's code and data availability statement is in the Data section.

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Data

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Data Availability Statement

Data is available as Supporting Information. RNA‐seq dataset is publicly available (GEO accession number GSE334684). All code and analysis scripts are publicly available in the GitHub repository (https://github.com/UmairSeemab/RNAseqData).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 9 MeSH terms, 4 funders, 62 references, 23 RRIDs.

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Vainionpää, K., Lappalainen, E., Nilsson, A., Pavela, I., Torsti, T., Seemab, U., Mäkinen, E., Shariatgorji, R., Andrén, P. E., Jalkanen, A., Forsberg, M. M., & Leinonen, H. (2026). Altered Retinal Dopamine Homeostasis in Murine Models of Retinitis Pigmentosa. Journal of neurochemistry, 170(9), e70552. https://doi.org/10.1111/jnc.70552

BibTeX

@article{vainionpaa2026altered,
author = {Vainionpää, Katri and Lappalainen, Eerik and Nilsson, Anna and Pavela, Ida and Torsti, Tommi and Seemab, Umair and Mäkinen, Elina and Shariatgorji, Reza and Andrén, Per E. and Jalkanen, Aaro and Forsberg, Markus M. and Leinonen, Henri},
title = {{Altered Retinal Dopamine Homeostasis in Murine Models of Retinitis Pigmentosa}},
journal = {Journal of neurochemistry},
year = {2026},
month = sep,
volume = {170},
number = {9},
pages = {e70552},
publisher = {Wiley},
issn = {0022-3042},
doi = {10.1111/jnc.70552},
url = {https://doi.org/10.1111/jnc.70552},
pmid = {42757827},
pmcid = {PMC13587835}
}

RIS

TY - JOUR
AU - Vainionpää, Katri
AU - Lappalainen, Eerik
AU - Nilsson, Anna
AU - Pavela, Ida
AU - Torsti, Tommi
AU - Seemab, Umair
AU - Mäkinen, Elina
AU - Shariatgorji, Reza
AU - Andrén, Per E.
AU - Jalkanen, Aaro
AU - Forsberg, Markus M.
AU - Leinonen, Henri
TI - Altered Retinal Dopamine Homeostasis in Murine Models of Retinitis Pigmentosa
T2 - Journal of neurochemistry
J2 - J Neurochem
PY - 2026
DA - 2026/09/01
VL - 170
IS - 9
SP - e70552
SN - 0022-3042
PB - Wiley
DO - 10.1111/jnc.70552
UR - https://doi.org/10.1111/jnc.70552
LA - en
ER -

CSL-JSON

{
"id": "10.1111/jnc.70552",
"type": "article-journal",
"title": "Altered Retinal Dopamine Homeostasis in Murine Models of Retinitis Pigmentosa",
"container-title": "Journal of neurochemistry",
"author": [
{
"family": "Vainionpää",
"given": "Katri"
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{
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{
"family": "Nilsson",
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{
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"given": "Ida"
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{
"family": "Torsti",
"given": "Tommi"
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{
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{
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{
"family": "Shariatgorji",
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},
{
"family": "Andrén",
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},
{
"family": "Jalkanen",
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},
{
"family": "Forsberg",
"given": "Markus M."
},
{
"family": "Leinonen",
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}
],
"container-title-short": "J Neurochem",
"volume": "170",
"issue": "9",
"page": "e70552",
"DOI": "10.1111/jnc.70552",
"PMID": "42757827",
"PMCID": "PMC13587835",
"ISSN": "0022-3042",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/jnc.70552",
"language": "en",
"issued": {
"date-parts": [
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}
}

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