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Dopamine and serotonin inversely modulate D2 medium spiny neurons to regulate cocaine reward.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Methods › RNAseq analysis ↔ CardozoPinto_Guo_2026_FigS1_code.Rmd, lines 424–460 · score 0.93 · Htr7.vMedpat, Mat.Dorsolateral_CPu, Mat.Ventromedial_CPu, Pat.Dorsolateral_CPu, Htr3a, Htr3b
  2. [2] § Results ↔ CardozoPinto_Guo_2026_FigS1_code.Rmd, lines 246–296 · score 0.74 · receptor families, Htr1d, Htr1b, Htr1f, Htr2a, Htr2c
  3. [3] § Methods › Fluorescence in situ hybridization mapping of 5HT receptor expression ↔ CardozoPinto_Guo_2026_FigS1_code.Rmd, lines 465–513 · score 0.72 · Htr1d, Htr1b, Drd1a, Htr1f, Htr2a, Htr2c
  4. [4] § Methods › Fluorescence in situ hybridization mapping of 5HT receptor expression ↔ CardozoPinto_Guo_2026_Fig1andS3_code.Rmd, lines 195–234 · score 0.64 · bar graphs, pMSN, highest expressing cell, receptor expression, subregions, HT
  5. [5] § Methods › Fluorescence in situ hybridization mapping of 5HT receptor expression ↔ CardozoPinto_Guo_2026_FigS2_code.Rmd, lines 122–157 · score 0.63 · bimodal distribution, Drd2 area covered, cutoff, DA receptor expression, chosen, Putative
  6. [6] § Results ↔ CardozoPinto_Guo_2026_FigS1_code.Rmd, lines 246–296 · score 0.58 · Htr1b, Htr1f, Htr2a, Htr2c, Gi, Gs

Paper

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The authors' code

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  1. ---
  2. title: "CardozoPinto_Guo_2026_FigS1_code"
  3. output: pdf_document
  4. ---
  6. #-------------------------------------------------------------------------------
  7. OVERVIEW
  8. #-------------------------------------------------------------------------------
  9. This notebook reproduces analyses shown in Sup Fig 1 of the publication:
  11. Cardozo Pinto D.F., Guo, M.Y., Pomrenze M.B., Morishita W., Li M.X., Zweifel L.S.
  12. Eshel N., Malenka R.C. Dopamine and serotonin inversely modulate D2 medium
  13. spiny neurons to regulate cocaine reward. Nat. Commun. (2026).
  15. Here, we take a previously published scRNAseq dataset of the mouse striatum
  16. and reanalyze it to identify which serotonin receptor genes are expressed on
  17. striatal medium spiny neurons. The dataset originated from the publication:
  19. Stanley G., Gokce O., Malenka R.C., Sudhof T.C., Quake S.R. Continuous and
  20. discrete neuron types of the adult murine striatum. Neuron (2020)
  22. And can be found at the link below:
  23. https://figshare.com/projects/Continuous_and_discrete_neuron_types_of_the_adult_murine_striatum/69080
  26. This script begins by loading the "metadata_final.csv" file from the "clustering
  27. and annotation metadata" folder and the "counts.csv" file from the "gene
  28. expression counts" folder of the FigShare repository above. Metadata_final has
  29. the metadata for each cell, with cells organized across rows. The counts file
  30. contains the raw gene counts for each medium spiny neuron, with cells organized
  31. in columns and genes organized across rows.
  34. #-------------------------------------------------------------------------------
  35. LOAD PACKAGES
  36. #-------------------------------------------------------------------------------
  37. ```{r}
  38. library(tidyverse)
  39. library(gplots)
  40. ```
  43. #-------------------------------------------------------------------------------
  44. SELECT THE DATA FILE AND LOAD IT: metadata_final.csv from Stanley et al 2020
  45. #-------------------------------------------------------------------------------
  46. ```{r}
  47. FigS1_metadata_path <- file.choose()
  49. metadata <- read.csv(FigS1_metadata_path, header=TRUE, check.names = FALSE)
  51. #the original metadata file has a duplicate cellname column in first position
  52. #remove the duplicate
  53. metadata <- metadata[,2:16]
  55. ```
  59. #-------------------------------------------------------------------------------
  60. SELECT THE DATA FILE AND LOAD IT: counts.csv from Stanley et al 2020
  61. #-------------------------------------------------------------------------------
  62. ```{r}
  63. FigS1_counts_path <- file.choose()
  65. counts <- read.csv(FigS1_counts_path, header=TRUE, check.names = FALSE)
  66. ```
  70. #-------------------------------------------------------------------------------
  71. JOIN THE COUNTS AND METADATA DATAFRAMES
  72. #-------------------------------------------------------------------------------
  73. ```{r}
  74. #organize counts data so that cells are in rows and genes are in columns
  75. #with the first column indicating the cell name as a factor
  76. gene_names <- counts[,1]
  77. counts <- t(counts[,-1])
  78. colnames(counts)<- gene_names
  79. counts <- as.data.frame(counts) %>%
  80. rownames_to_column(var="cell") %>%
  81. mutate(cell = as.factor(cell))
  83. #metadata file already has cells organized in rows
  84. #make the cell name a factor in the metadata as well
  85. metadata <- metadata %>% rename(cell=cell.name) %>% mutate(cell = as.factor(cell))
  87. #join counts and metadata files by cell name factor
  88. #notice that we lose 4 cells which are present in the counts file but not
  89. #the metadata_final file. These are astrocyte- contaminated cells as shown in
  90. #Stanley at al Sup Fig 1c
  91. FigS1_data <- inner_join(counts, metadata, by="cell")
  92. ```
  95. #-------------------------------------------------------------------------------
  96. ORGANIZE DATA, SET TYPES, CLEAN UP WORKSPACE
  97. #-------------------------------------------------------------------------------
  98. ```{r}
  99. FigS1_data <- FigS1_data %>%
  100. relocate(cell, nGene, nReads, percent.ribo, seq.plate, expt.name, i7.index,
  101. i5.index, submission.ID, expt.num, sort.well, sort.plate.idx,
  102. discrete.type, subcluster, continuous.subtype) %>%
  103. rename(num.gene = nGene,
  104. num.reads= nReads,
  105. mouse.id = expt.name, #these are mouse IDs, see Stanley et al Sup Fig 1L
  106. submission.id = submission.ID) %>%
  107. mutate(cell = as.factor(cell)) %>%
  108. mutate(seq.plate = as.factor(seq.plate)) %>%
  109. mutate(mouse.id = as.factor(mouse.id)) %>%
  110. mutate(submission.id = as.factor(submission.id)) %>%
  111. mutate(expt.num = as.factor(expt.num)) %>%
  112. mutate(sort.well = as.factor(sort.well)) %>%
  113. mutate(sort.plate.idx = as.factor(sort.plate.idx)) %>%
  114. mutate(discrete.type = as.factor(discrete.type)) %>%
  115. mutate(subcluster = as.factor(subcluster)) %>%
  116. mutate(continuous.subtype = as.factor(continuous.subtype)) %>%
  117. pivot_longer(cols = c(16:ncol(FigS1_data)), names_to = "gene", values_to = "count") %>%
  118. mutate(gene = as.factor(gene))
  122. #clean up the workspace
  123. rm(counts, metadata, FigS1_counts_path, FigS1_metadata_path, gene_names)
  124. ```
  128. #-------------------------------------------------------------------------------
  129. FILTER OUT CELLS FROM OLF TUB AND ICJ REGIONS
  130. #-------------------------------------------------------------------------------
  131. ```{r}
  132. FigS1_data <- FigS1_data %>%
  133. filter(continuous.subtype != "ICj") %>%
  134. filter(continuous.subtype != "OT.ruffle") %>%
  135. filter(continuous.subtype != "OT.flat") %>%
  136. droplevels()
  137. ```
  141. #-------------------------------------------------------------------------------
  142. FILTER OUT GENES THAT ARE NOT OF INTEREST FOR THIS ANALYSIS
  143. #-------------------------------------------------------------------------------
  144. ```{r}
  145. FigS1_data <- FigS1_data %>%
  146. dplyr::select(cell, mouse.id, num.reads, discrete.type, continuous.subtype, gene, count) %>%
  147. filter(
  148. gene == "Drd1a" |gene == "Pdyn" | gene == "Tac1" | #D1 MSN markers
  149. gene == "Drd2" | gene == "Penk" | gene == "Adora2a" | #D2 MSN markers
  150. gene == "Tac2" | gene == "Nxph4" | gene == "Pcdh8" | #D1H MSN markers
  151. gene == "Kremen1" | gene == "Sema5b" | #patch markers
  152. gene == "Id4" | gene == "Igfbp4" | gene == "Calb1" | #matrix markers
  153. gene == "Htr1a" | gene == "Htr1b" | gene == "Htr1d" | gene == "Htr1e" | #5HTRs
  154. gene == "Htr1f" | gene == "Htr2a" | gene == "Htr2b" | gene == "Htr2c" | #5HTRs
  155. gene == "Htr3a" | gene == "Htr3b" | gene == "Htr4" | gene == "Htr5a" | #5HTRs
  156. gene == "Htr5b" | gene == "Htr6" | gene == "Htr7" #5HTRs
  157. )
  158. ```
  162. The code above throws a warning which says that the genes Htr1e, Htr3a,and
  163. Htr3b were not found in the dataset. This makes sense for Htr1e because mice do
  164. not have 5HT1e serotonin receptors. reference:
  166. Bai et al. Molecular cloning and pharmacological characterization of the guinea
  167. pig 5-HT1e receptor. Eur J Pharm 2004
  169. However, mice do have 5HT3 receptors, which are made up of Htr3a and Htr3b
  170. subunits. The absence of these genes in the dataset of medium spiny neurons
  171. indicates that these genes are not expressed in any MSNs, so the next piece of
  172. code assigns each cell a count of 0 for these genes. This is consistent with
  173. reports of 5HT3 receptors as cell-type specific markers for distinguishing
  174. striatal GABAergic interneurons from MSNs. References:
  176. Muñoz-Manchado, A. B. et al. Novel Striatal GABAergic Interneuron Populations
  177. Labeled in the 5HT3a EGFP Mouse. Cerebral Cortex 26, 96–105 (2016).
  179. Assous, M. et al. Identification and Characterization of a Novel Spontaneously
  180. Active Bursty GABAergic Interneuron in the Mouse Striatum. The Journal of
  181. Neuroscience 38, 5688–5699 (2018).
  183. ```{r}
  184. FigS1_data <- FigS1_data %>%
  185. pivot_wider(names_from = gene, values_from = count) %>%
  186. mutate(Htr3a = 0, Htr3b = 0) %>%
  187. pivot_longer(names_to = "gene", values_to = "count", cols = c(6:33)) %>%
  188. mutate(gene = as.factor(gene))%>%
  189. group_by(cell) %>%
  190. mutate(cpm = count/num.reads*1000000)
  191. ```
  195. #-------------------------------------------------------------------------------
  196. GROUP GENES OF INTEREST INTO USEFUL CATEGORIES
  197. #-------------------------------------------------------------------------------
  198. ```{r}
  199. FigS1_data <- FigS1_data %>%
  200. mutate(gene_type = fct_collapse(gene,
  201. d1_markers = c("Drd1a", "Pdyn", "Tac1"),
  202. d2_markers = c("Drd2", "Penk", "Adora2a"),
  203. d1h_markers = c("Tac2", "Nxph4", "Pcdh8"),
  204. patchmatrix_markers = c("Kremen1", "Sema5b",
  205. "Id4", "Igfbp4", "Calb1"),
  206. htrs = c("Htr1a", "Htr1b", "Htr1d",
  207. "Htr1f", "Htr2a", "Htr2b", "Htr2c",
  208. "Htr3a", "Htr3b", "Htr4", "Htr5a",
  209. "Htr5b", "Htr6", "Htr7"),
  210. other_level = "other"))
  211. ```
  215. #-------------------------------------------------------------------------------
  216. PLOT PERCENTAGE OF MSNs POSITIVE FOR ANY 5HTR GENE
  217. #-------------------------------------------------------------------------------
  218. This piece of code generates the data shown in Sup Fig 1a, which was then
  219. imported into Graphpad Prism to be plotted in a style matching the rest of the
  220. figures in the paper
  221. ```{r}
  223. FigS1a_data <- FigS1_data %>%
  224. filter(gene_type == "htrs") %>%
  225. droplevels() %>%
  226. group_by(mouse.id, cell, gene_type) %>%
  227. summarize(count = sum(count)) %>%
  228. ungroup() %>%
  229. group_by(mouse.id, gene_type) %>%
  230. summarize(pct_positive = sum(count > 0)/n()*100,
  231. num_cell = n())
  234. FigS1a_data %>%
  235. ggplot(aes(x=gene_type, y=pct_positive)) +
  236. stat_summary(fun = mean, geom = "col", size =0.5) +
  237. stat_summary(fun.data = mean_se, geom = "errorbar", width=0.5, size=1)+
  238. geom_point(aes(y= pct_positive), size =2, color="gray", fill = "gray")+
  239. theme_classic()+
  240. scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
  241. ggtitle("percentage of MSNs positive for any 5HTR gene")
  242. ```
  246. #-------------------------------------------------------------------------------
  247. PLOT PERCENTAGE OF MSNs POSITIVE FOR ANY 5HTR GENE, BY RECEPTOR FAMILY
  248. #-------------------------------------------------------------------------------
  249. This piece of code generates the data shown in Sup Fig 1b, which was then
  250. imported into Graphpad Prism to be plotted in a style matching the rest of the
  251. figures in the paper and for additional statistical testing.
  252. ```{r}
  253. FigS1b_data <- FigS1_data %>%
  254. filter(gene_type == "htrs") %>%
  255. droplevels() %>%
  256. mutate(family = fct_collapse(gene,
  257. Gi = c("Htr1a",
  258. "Htr1b",
  259. "Htr1d",
  260. "Htr1f",
  261. "Htr5a",
  262. "Htr5b"),
  263. Gq = c("Htr2a",
  264. "Htr2b",
  265. "Htr2c"),
  266. Gs = c("Htr4",
  267. "Htr6",
  268. "Htr7"),
  269. ion = c("Htr3a",
  270. "Htr3b"),
  271. other_level = "other")) %>%
  272. group_by(mouse.id, cell, gene_type, family) %>%
  273. summarize(count = sum(count)) %>%
  274. ungroup() %>%
  275. group_by(mouse.id, gene_type,family) %>%
  276. summarize(pct_positive = sum(count > 0)/n()*100,
  277. num_cell = n())
  281. #reorder factor
  282. FigS1b_data$family <- factor(FigS1b_data$family, levels=c("Gi", "Gq", "Gs", "ion"))
  286. FigS1b_data %>%
  287. ggplot(aes(x=family, y=pct_positive)) +
  288. stat_summary(fun = mean, geom = "col", size =0.5) +
  289. stat_summary(fun.data = mean_se, geom = "errorbar", width=0.5, size=1)+
  290. geom_point(aes(y= pct_positive), size =2, color="gray", fill = "gray")+
  291. theme_classic()+
  292. ylim(0,100)+
  293. scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
  294. ggtitle("percentage of MSNs positive for 5HTR genes by receptor family")
  296. ```
  300. #-------------------------------------------------------------------------------
  301. PLOT 5HTR GENE EXPRESSION BY RECEPTOR FAMILY
  302. #-------------------------------------------------------------------------------
  303. This piece of code generates the data shown in Sup Fig 1c, which was then
  304. imported into Graphpad Prism to be plotted in a style matching the rest of the
  305. figures in the paper and for additional statistical testing.
  306. ```{r}
  307. FigS1c_data <- FigS1_data %>%
  308. filter(gene_type == "htrs") %>%
  309. droplevels() %>%
  310. mutate(family = fct_collapse(gene,
  311. Gi = c("Htr1a",
  312. "Htr1b",
  313. "Htr1d",
  314. "Htr1f",
  315. "Htr5a",
  316. "Htr5b"),
  317. Gq = c("Htr2a",
  318. "Htr2b",
  319. "Htr2c"),
  320. Gs = c("Htr4",
  321. "Htr6",
  322. "Htr7"),
  323. ion = c("Htr3a",
  324. "Htr3b"),
  325. other_level = "other")) %>%
  326. mutate(num.reads = as.factor(num.reads)) %>%
  327. group_by(mouse.id, cell, family, gene_type, num.reads) %>%
  328. summarize(count = sum(count)) %>%
  329. mutate(num.reads = as.numeric(as.character(num.reads))) %>%
  330. ungroup() %>%
  331. group_by(mouse.id, family, cell, gene_type) %>%
  332. mutate(cpm = count/num.reads*1000000)%>%
  333. ungroup() %>%
  334. group_by(mouse.id, family, gene_type) %>%
  335. summarize(cpm = (mean(cpm)))
  338. #reorder factor
  339. FigS1c_data$family <- factor(FigS1c_data$family, levels=c("Gi", "Gq", "Gs", "ion"))
  342. FigS1c_data %>%
  343. ggplot(aes(x=family, y=log10(cpm+1))) +
  344. stat_summary(fun = mean, geom = "col", size =0.5) +
  345. stat_summary(fun.data = mean_se, geom = "errorbar", width=0.5, size=1)+
  346. geom_point(aes(y= log10(cpm+1)), size =2, color="gray", fill = "gray")+
  347. theme_classic()+
  348. ggtitle("5HTR gene expression by receptor family")
  350. ```
  354. #-------------------------------------------------------------------------------
  355. PLOT PERCENTAGE OF MSNs POSITIVE FOR EACH 5HTR GENEE
  356. #-------------------------------------------------------------------------------
  357. This piece of code generates the data shown in Sup Fig 1d, which was then
  358. imported into Graphpad Prism to be plotted in a style matching the rest of the
  359. figures in the paper and for additional statistical analysis
  360. ```{r}
  361. FigS1d_data <- FigS1_data %>%
  362. filter(gene_type == "htrs") %>%
  364. droplevels() %>%
  365. group_by(mouse.id, gene_type, gene) %>%
  366. summarize(num_pos = sum(count > 0),
  367. num_cell = n(),
  368. pct_positive = num_pos/num_cell*100)
  372. #reorder factor
  373. FigS1d_data$gene <- factor(FigS1d_data$gene, levels=c("Htr1d", "Htr1b", "Htr1f", "Htr5a", "Htr5b",
  374. "Htr1a", "Htr2c", "Htr2a", "Htr2b", "Htr4",
  375. "Htr6", "Htr7", "Htr3a", "Htr3b"))
  379. FigS1d_data %>%
  380. ggplot(aes(x=gene, y=pct_positive)) +
  381. stat_summary(fun = mean, geom = "col", size =0.5) +
  382. stat_summary(fun.data = mean_se, geom = "errorbar", width=0.5, size=1)+
  383. geom_point(aes(y= pct_positive), size =2, color="gray", fill = "gray")+
  384. theme_classic()+
  385. ylim(0,100)+
  386. scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100), limits = c(0,100))+
  387. ggtitle("percentage of MSNs positive for 5HTR genes")
  388. ```
  391. #-------------------------------------------------------------------------------
  392. PLOT 5HTR GENE EXPRESSION BY GENE
  393. #-------------------------------------------------------------------------------
  394. This piece of code generates the data shown in Sup Fig 1e, which was then
  395. imported into Graphpad Prism to be plotted in a style matching the rest of the
  396. figures in the paper and for additional statistical testing.
  398. ```{r}
  399. FigS1e_data <- FigS1_data %>%
  400. filter(gene_type == "htrs") %>%
  401. droplevels() %>%
  402. group_by(mouse.id, gene_type, gene) %>%
  403. summarize(cpm = mean(cpm))
  407. #reorder factor
  408. FigS1e_data$gene <- factor(FigS1e_data$gene, levels=c("Htr1d", "Htr1b", "Htr1f", "Htr5a", "Htr5b",
  409. "Htr1a", "Htr2c", "Htr2a", "Htr2b", "Htr4",
  410. "Htr6", "Htr7", "Htr3a", "Htr3b"))
  412. FigS1e_data %>%
  413. ggplot(aes(x=gene, y=log10(cpm+1))) +
  414. stat_summary(fun = mean, geom = "col", size =0.5) +
  415. stat_summary(fun.data = mean_se, geom = "errorbar", width=0.5, size=1)+
  416. geom_point(aes(y= log10(cpm+1)), size =2, color="gray", fill = "gray")+
  417. theme_classic()+
  418. ylim(0,2.1)+
  419. ggtitle("5HTR gene expression")
  420. ```
  424. #-------------------------------------------------------------------------------
  425. PLOT 5HTR GENE EXPRESSION BY STRIATAL COMPARTMENT
  426. #-------------------------------------------------------------------------------
  427. This piece of code generates the data shown in Sup Fig 1f, which was then
  428. imported into Graphpad Prism to be plotted in a style matching the rest of the
  429. figures in the paper and for additional statistical testing.
  430. ```{r}
  431. FigS1f_data <- FigS1_data %>%
  432. filter(gene_type == "htrs") %>%
  433. droplevels() %>%
  434. mutate(compartment = fct_collapse(continuous.subtype,
  435. patch = c("Htr7.vMedpat",
  436. "Pat.Dorsolateral_CPu",
  437. "Pat.Ventromedial"),
  438. matrix = c("Mat.Dorsolateral_CPu",
  439. "Mat.Ventromedial_CPu"),
  440. other_level = "other")) %>%
  441. filter(compartment != "other") %>%
  442. group_by(mouse.id, compartment, gene_type, gene) %>%
  443. summarize(cpm = mean(cpm))
  445. #reorder factor
  446. FigS1f_data$gene <- factor(FigS1f_data$gene, levels=c("Htr1d", "Htr1b", "Htr1f", "Htr5a", "Htr5b",
  447. "Htr1a", "Htr2c", "Htr2a", "Htr2b", "Htr4",
  448. "Htr6", "Htr7", "Htr3a", "Htr3b"))
  449. dodge_width <- 0.75
  451. FigS1f_data %>%
  452. ggplot(aes(x=gene, y=log10(cpm+1), group=compartment, fill=compartment)) +
  453. stat_summary(fun = mean, geom = "col", size =0.5, position = position_dodge(width=dodge_width)) +
  454. stat_summary(fun.data = mean_se, geom = "errorbar", width=0.5, size=1, position = position_dodge(width=dodge_width))+
  455. geom_point(aes(y= log10(cpm+1)), size =2, color="gray", fill = "gray", position = position_dodge(width=dodge_width))+
  456. theme_classic()+
  457. ylim(0,2.1)+
  458. ggtitle("5HTR gene expression")
  460. ```
  465. #-------------------------------------------------------------------------------
  466. GENE COEXPRESSION ANALYSIS
  467. #-------------------------------------------------------------------------------
  468. This code reproduces the correlation heatmap in Sup Fig 1g
  470. ```{r}
  471. #start by selecting genes of interest, calculating log cpm for each gene, and pivoting to wide form data frame
  473. FigS1g_data <- FigS1_data %>%
  474. mutate(log.cpm = log10(cpm+1)) %>% #calculate log cpm
  475. dplyr::select(cell, gene, log.cpm) %>%
  476. filter(
  477. gene == "Drd1a" |gene == "Pdyn" | gene == "Tac1" | #D1 markers
  478. gene == "Drd2" | gene == "Penk" | gene == "Adora2a" | #D2 markers
  479. # gene == "Htr1a" | #exclude because count value for this gene = 0 for all cells
  480. gene == "Htr1b" |
  481. gene == "Htr1d" |
  482. gene == "Htr1e" |
  483. gene == "Htr1f" |
  484. gene == "Htr2a" |
  485. gene == "Htr2b" |
  486. gene == "Htr2c" |
  487. # gene == "Htr3a" | #exclude because count value for this gene = 0 for all cells
  488. # gene == "Htr3b" | #exclude because count value for this gene = 0 for all cells
  489. gene == "Htr4" |
  490. gene == "Htr5a" |
  491. # gene == "Htr5b" |#exclude because count value for this gene = 0 for all cells
  492. gene == "Htr6" |
  493. gene == "Htr7"
  494. ) %>%
  495. pivot_wider(names_from = gene, values_from = log.cpm) %>%
  496. relocate(cell,
  497. Drd1a, Tac1, Pdyn,
  498. Penk, Adora2a, Drd2,
  499. Htr6, Htr1f, Htr1d, Htr5a,
  500. Htr2c, Htr4, Htr1b, Htr7, Htr2a, Htr2b)
  502. #calculate correlation matrix
  503. cormat <- cor(scale(FigS1g_data[,c(2:ncol(FigS1g_data))]), method = "spearman")
  506. #set some parameters for plotting the heatmap, and choose which correlations to plot
  507. par(mar = c(1, 1, 1, 1))
  508. dispmat <- t(cormat[,1:6])
  511. #plot the correlation heatmap
  512. (FigS1g_plot <- heatmap.2(dispmat, trace="none", col=bluered(100), density.info = "none", keysize= 0.5, symkey = TRUE, revC = TRUE, key.xlab = "Spearman's r", dendrogram = "both", scale = "none", lwid=c(10,20), lhei=c(6,12), cexRow = .75, cexCol = .75, main="GENE COEXPRESSION ANALYSIS", Colv = FALSE, Rowv = TRUE, na.rm = TRUE))
  513. ```

CardozoPinto_Guo_2026_FigS1_code.Rmd at commit 6dae371, no license · at the source

Overview

Authors: Daniel F Cardozo Pinto1,2,3, Michaela Y Guo1, Matthew B Pomrenze1, Wade Morishita1, Mona X Li1, Larry S Zweifel4,5, Neir Eshel6, Robert C Malenka1
ORCID iDs: Daniel F Cardozo Pinto, Michaela Y Guo, Matthew B Pomrenze, Mona X Li, Larry S Zweifel, Neir Eshel, Robert C Malenka
  1. Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA
  2. Present Address: Society of Fellows, Harvard University, Cambridge, MA USA
  3. Present Address: Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA USA
  4. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA USA
  5. Department of Pharmacology, University of Washington, Seattle, WA USA
  6. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA
Institutions: Harvard University (United States); Stanford Medicine (United States); Stanford University (United States); University of Washington (United States)
Journal: Nature communications, volume 17, issue 1, article 3964
Dates: received 5 May 2025; accepted 26 February 2026; published online 14 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70519-8 · PMID 41832154 · PMCID PMC13133351 · OpenAlex W7135389760
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Excitability, Neural circuits
MeSH: Cocaine*, Dopamine*, Medium Spiny Neurons*, Receptors, Dopamine D2*, Reward*, Serotonin*, Animals, Corpus Striatum, Male, Mice, Mice, Inbred C57BL, Neurons, Receptor, Serotonin, 5-HT2A, Receptor, Serotonin, 5-HT2C (* major topic)
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (K08 MH123791, R01 MH138645); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (K08MH123791, K99DA056573, P50DA042012, P30DA048736, R01MH138645); Burroughs Wellcome Fund (BWF) (Career Award for Medical Scientists); NIDA NIH HHS (K99 DA056573, P50 DA042012, P30 DA048736); Simons Foundation (Bridge to Independence Award); Brain and Behavior Research Foundation (Brain & Behavior Research Foundation) (Young Investigator Grant); Howard Hughes Medical Institute (HHMI) (Gilliam Fellowship)
Citations: not cited yet (Europe PMC); 91 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 6 matches between paragraphs and lines of code.

dcardozop/CardozoPinto_Guo_2026_NatCommun

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6dae371f6a68252a920d4400738b8a5256d54727, 23 January 2026
Languages: R (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 5 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/dcardozop/CardozoPinto_Guo_2026_NatCommun

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-70519-8.

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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:

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Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 14 MeSH terms, 7 funders, 86 references.

Cite

This paper

Cardozo Pinto, D. F., Guo, M. Y., Pomrenze, M. B., Morishita, W., Li, M. X., Zweifel, L. S., Eshel, N., & Malenka, R. C. (2026). Dopamine and serotonin inversely modulate D2 medium spiny neurons to regulate cocaine reward. Nature communications, 17(1), 3964. https://doi.org/10.1038/s41467-026-70519-8

BibTeX

@article{cardozopinto2026dopamine,
author = {Cardozo Pinto, Daniel F and Guo, Michaela Y and Pomrenze, Matthew B and Morishita, Wade and Li, Mona X and Zweifel, Larry S and Eshel, Neir and Malenka, Robert C},
title = {{Dopamine and serotonin inversely modulate D2 medium spiny neurons to regulate cocaine reward}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3964},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70519-8},
url = {https://doi.org/10.1038/s41467-026-70519-8},
pmid = {41832154},
pmcid = {PMC13133351}
}

RIS

TY - JOUR
AU - Cardozo Pinto, Daniel F
AU - Guo, Michaela Y
AU - Pomrenze, Matthew B
AU - Morishita, Wade
AU - Li, Mona X
AU - Zweifel, Larry S
AU - Eshel, Neir
AU - Malenka, Robert C
TI - Dopamine and serotonin inversely modulate D2 medium spiny neurons to regulate cocaine reward
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/14
VL - 17
IS - 1
SP - 3964
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70519-8
UR - https://doi.org/10.1038/s41467-026-70519-8
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Dopamine and serotonin inversely modulate D2 medium spiny neurons to regulate cocaine reward",
"container-title": "Nature communications",
"author": [
{
"family": "Cardozo Pinto",
"given": "Daniel F"
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{
"family": "Guo",
"given": "Michaela Y"
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{
"family": "Morishita",
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}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "3964",
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"PMCID": "PMC13133351",
"ISSN": "2041-1723",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
14
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]
}
}

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