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Repetitive neuronal activation regulates cellular maturation state via nuclear reprogramming.

Code ↔ Paper

2 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 2 matches
  1. [1] § Results › REPOPS initiates long-term anti-depressive behavioral changes in mice ↔ Fig3d_Tail Suspension test/scripts/TailSuspension.R, lines 147–163 · score 0.59 · tail suspension, post hoc, way ANOVA, HSD, Tukey, Immobility
  2. [2] § Methods › Statistical analyses ↔ Fig3ab_Open Field test/scripts/OpenField.R, lines 62–109 · score 0.57 · post hoc, way ANOVA, mouse ID, Bonferroni

Paper

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The authors' code

R · 163 lines · 4.9 KB · MIT · 1 match

  1. rm(list = ls())
  3. ############################################
  4. # Packages
  5. ############################################
  6. pkgs_cran <- c("readxl", "openxlsx", "ggplot2", "tidyr", "dplyr", "reshape2", "rstatix", "tibble")
  8. for (pkg in pkgs_cran) {
  9. if (!requireNamespace(pkg, quietly = TRUE)) {
  10. install.packages(pkg, dependencies = TRUE)
  11. }
  12. library(pkg, character.only = TRUE)
  13. }
  15. ############################################
  16. # Set Working Directory
  17. ############################################
  18. setwd("~/Desktop/")
  21. ############################################
  22. # Line Plot for Time Bins
  23. ############################################
  24. Data <- read_excel("TailSuspension_Data.xlsx", sheet = "TS")
  25. Data <- Data[, -c(1, 19)] # Remove dead mouse
  27. sem <- function(x) sd(x) / sqrt(length(x))
  29. Data_mean <- cbind(
  30. apply(Data[, 1:10], 1, mean),
  31. apply(Data[, 11:19], 1, mean),
  32. apply(Data[, 20:29], 1, mean)
  33. )
  35. Data_sem <- cbind(
  36. apply(Data[, 1:10], 1, sem),
  37. apply(Data[, 11:19], 1, sem),
  38. apply(Data[, 20:29], 1, sem)
  39. )
  41. df <- data.frame(
  42. Time = 1:10,
  43. StimType = rep(c("NoStim", "Stimx3+2wks", "Stimx10+2wks"), each = 10),
  44. Mean = melt(Data_mean)[, 3],
  45. SEM = melt(Data_sem)[, 3]
  46. )
  48. g <- ggplot(df, aes(x = Time, y = Mean, color = StimType, group = StimType)) +
  49. geom_line(linewidth = 1.2) +
  50. geom_point(size = 5) +
  51. geom_errorbar(aes(ymin = Mean - SEM, ymax = Mean + SEM), width = 0.5, linewidth = 1) +
  52. theme_classic(base_size = 30) +
  53. theme(
  54. panel.grid = element_blank(),
  55. axis.title = element_text(size = 25),
  56. axis.text = element_text(size = 25, colour = "black"),
  57. axis.ticks = element_line(linewidth = 1.5),
  58. legend.position = "none"
  59. ) +
  60. scale_colour_manual(values = c("black", "red", "blue")) +
  61. scale_fill_manual(values = c("black", "red", "blue")) +
  62. scale_x_continuous(breaks = 1:10, labels = as.character(1:10)) +
  63. scale_y_continuous(expand = c(0, 0), breaks = seq(0, 100, 20), limits = c(0, 100)) +
  64. xlab("Blocks of 1 min") +
  65. ylab("Immobility (%)")
  67. ggsave("TailSuspension.png", g, width = 6, height = 6, dpi = 300)
  69. ############################################
  70. # Repeated Measures ANOVA
  71. ############################################
  72. Data$timebin <- 1:10
  74. data_long <- pivot_longer(
  75. Data,
  76. cols = -timebin,
  77. names_to = "MouseID",
  78. values_to = "Immobility"
  79. )
  81. data_long$StimType <- rep(
  82. c(rep("NoStim", 10), rep("Stimx3+2wks", 9), rep("Stimx10+2wks", 10)),
  83. times = 10
  84. )
  86. res.aov <- anova_test(
  87. data = data_long,
  88. dv = Immobility,
  89. wid = MouseID,
  90. between = StimType,
  91. within = timebin
  92. )
  94. anova <- get_anova_table(res.aov)
  96. pwc1 <- data_long %>%
  97. group_by(timebin) %>%
  98. pairwise_t_test(Immobility ~ StimType, paired = FALSE, p.adjust.method = "bonferroni")
  100. pwc2 <- data_long %>%
  101. pairwise_t_test(Immobility ~ StimType, paired = FALSE, p.adjust.method = "bonferroni")
  103. write.xlsx(
  104. list(ANOVA_Result = anova, Bonf_Result1 = pwc1, Bonf_Result2 = pwc2),
  105. "TailSuspension_Stats.xlsx"
  106. )
  108. ############################################
  109. # Group Average Boxplot
  110. ############################################
  111. Data <- read_excel("TailSuspension_Data.xlsx", sheet = "TS")
  112. Data <- Data[, -c(1, 19)] # Remove dead mouse
  114. Data <- data.frame(
  115. Immobility = colMeans(Data),
  116. Group = c(rep("NoStim", 10), rep("Stimx3+2wks", 9), rep("Stimx10+2wks", 10))
  117. )
  119. Data$Group <- factor(Data$Group, levels = c("NoStim", "Stimx3+2wks", "Stimx10+2wks"))
  121. Data1 <- melt(Data, id.vars = "Group", value.name = "Immobility")
  123. g <- ggplot(Data1, aes(y = Immobility, x = Group, colour = Group, fill = Group)) +
  124. geom_boxplot(size = 1, width = 0.8, alpha = 0.5) +
  125. geom_point(shape = 21, size = 4, color = "black", alpha = 1, stroke = 0.75, show.legend = FALSE) +
  126. theme_classic(base_size = 24) +
  127. theme(
  128. legend.position = "none",
  129. axis.text.x = element_blank(),
  130. axis.text.y = element_text(colour = "black"),
  131. axis.title.x = element_blank(),
  132. axis.title.y = element_text(colour = "black")
  133. ) +
  134. coord_cartesian(xlim = c(0.4, 3.6), expand = FALSE) +
  135. guides(
  136. fill = guide_legend(override.aes = list(color = "transparent")),
  137. color = "none",
  138. shape = "none"
  139. ) +
  140. scale_fill_manual(values = c("gray30", "blue", "red")) +
  141. scale_color_manual(values = c("gray30", "blue", "red")) +
  142. scale_y_continuous(limits = c(30, 80), breaks = seq(30, 80, 10)) +
  143. ylab("Immobility (average, %)")
  145. ggsave("TailSuspension_ave.png", g, width = 3.5, height = 5, dpi = 300)
  147. ############################################
  148. # One-Way ANOVA and Tukey Post-hoc
  149. ############################################
  150. anova_result <- aov(Immobility ~ Group, data = Data1)
  151. tukey_result <- TukeyHSD(anova_result)
  153. a <- summary(anova_result)[[1]] %>% rownames_to_column("name")
  154. t <- data.frame(tukey_result[[1]]) %>% rownames_to_column("name")
  156. write.xlsx(
  157. list(ANOVA_Result = a, Tukey_Result = t),
  158. "TailSuspension_ave_Stats.xlsx"
  159. )
  161. ############################################
  162. # End of program
  163. ############################################

TailSuspension.R at commit 91d79a5, under MIT · at the source

Overview

Authors: Tomoyuki Murano1, Hideo Hagihara1, Katsunori Tajinda2, Keizo Takao3,4, Yoshihiro Takamiya1, Kaoru Katoh5,6,7,8,9, Alfred J Robison10, Mitsuyuki Matsumoto2,11, Masakazu Namihira5,8,12, Tsuyoshi Miyakawa1
ORCID iDs: Tomoyuki Murano, Hideo Hagihara, Keizo Takao, Kaoru Katoh, Mitsuyuki Matsumoto, Masakazu Namihira, Tsuyoshi Miyakawa
  1. Division of Systems Medical Science, Center for Medical Science, Fujita Health University, Toyoake, Japan
  2. Astellas Research Institute of America, San Diego, CA USA
  3. Department of Behavioral Physiology, Faculty of Medicine, University of Toyama, Toyama, Japan
  4. Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
  5. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
  6. Ph.D. Program in Humanics, School of Integrative and Global Majors, University of Tsukuba, Tsukuba, Japan
  7. The Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
  8. Molecular Biosystem Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
  9. Department of Biochemistry and Cell Biology, National Institute of Infectious Diseases (NIID), Tokyo, Japan
  10. Department of Physiology and Neuroscience Program, Michigan State University, East Lansing, MI USA
  11. Arialys Therapeutics Inc., La Jolla, CA USA
  12. Laboratory of Neural Regeneration and Brain Repair, Division of Biological Science, Graduate School of Science andTechnology, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama-cho, Ikoma, Nara, Japan
Institutions: Fujita Health University (Japan); University of Toyama (Japan); University of Tsukuba (Japan); National Institutes of Natural Sciences (Japan); National Institute of Infectious Diseases (Japan); The Exploratory Research Center on Life and Living Systems (Japan); Artificial Intelligence Research Center (Japan); National Institute of Advanced Industrial Science and Technology (Japan); Michigan State University (United States); Nara Institute of Science and Technology (Japan)
Journal: Nature communications, volume 17, issue 1, article 5881
Dates: received 12 May 2025; accepted 29 May 2026; published online 17 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74202-w · PMID 42469214 · PMCID PMC13379389 · OpenAlex W4410192963
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.02.651848
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging, Machine learning
Keywords: Cellular neuroscience, Epigenetics and plasticity
MeSH: Cellular Reprogramming*, Dentate Gyrus*, Neurons*, Animals, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Optogenetics (* major topic)
Topic: Pluripotent Stem Cells Research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Japan Agency for Medical Research and Development (AMED) (JP18dm0107101, JP21dm0207111)
Citations: not cited yet (Europe PMC); 135 references in the paper
Research resources: HA-tag RRID:AB_1549585, Calbindin RRID:AB_2068199, JunD RRID:AB_2130167, NeuN RRID:AB_2149209, For Lamin B1 staining RRID:AB_2281281, Cyclin B RRID:AB_2536863, Calbindin RRID:AB_2571569, ΔFosB RRID:AB_2798577, tri-methyl-Histone H3 (Lys9me3 RRID:AB_2887767, phospho-Histone H3 RRID:AB_310177, GFAP RRID:AB_477035, Iba1 RRID:AB_839504, POMC-Cre RRID:IMSR_JAX:010714, ROSA26-CAG-stopflox-ChR2(H134R)-EYFP RRID:IMSR_JAX:012569

Abstract

Neural stimulation, such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS), is highly effective clinical intervention for a broad spectrum of psychiatric disorders, including depression and schizophrenia. However, their mechanism of action at the cellular level remains poorly understood. Here, we model ECT with repeated optogenetic neuronal stimulation in the mouse dentate gyrus, and observe ECT-relevant behavioral changes, including decreased depression-like behavior and increased locomotor activity. At the cellular level, we identify dematuration to a long-term stable state, persisting for more than one month, defined by changes in nuclear structure, gene expression patterns resembling the G2/M phase of the cell cycle, and altered neural coding of navigational information. Moreover, knockout of the G2/M master regulator Cyclin B attenuates some of behavioral and cellular effects. These findings demonstrate that chronically-repeated brain stimulation triggers plasticity of the cellular state, revealing a form of stimulus-regulated nuclear reprogramming with potential clinical utility.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

tmurano/REPeatedOPtogeneticStimulation

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 91d79a5a47ae112013469f4eca0f79e378f10f18, 7 January 2026
Languages: R (15), Python (6), Jupyter (2)
Size: 382 files, 23 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, CITATION.cff, 2 notebooks
Not found: environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), Matplotlib (4 files), pandas (4 files), SciPy (4 files), DESeq2 (2 files), Keras (2 files), scikit-learn (2 files), seaborn (2 files), tidyverse (2 files), XGBoost (2 files), broom (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
25 files
At the source: github.com/tmurano/REPeatedOPtogeneticStimulation

Zenodo 19904295

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.19904295

Code availability

The custom code used for RNA-seq, ATAC-seq, and Ca²⁺ imaging analyses in this study is publicly available on GitHub [https://github.com/tmurano/REPeatedOPtogeneticStimulation] and is archived on Zenodo with a citable DOI (https://doi.org/10.5281/zenodo.19904295).

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 23 scripts, each with its path and the digest of its content;
  • 2 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 RNA-seq and ATAC-seq data generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession codes GSE227200 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227200) and GSE227201 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227201), respectively. The post-mortem human dentate gyrus RNA-seq data reanalyzed in this study are available in the Sequence Read Archive under accession SRP241159 (https://www.ncbi.nlm.nih.gov/sra/?term=SRP241159). The de-identified ECT history metadata linked to the SRP241159 samples were provided by Astellas Pharma Inc. and are not available for external redistribution. The raw in vivo Ca²⁺ imaging data have been deposited in the Systems Science of Biological Dynamics (SSBD) repository at RIKEN (https://ssbd.riken.jp/repository/335). The confocal image data underlying the histological quantifications are available at figshare [10.6084/m9.figshare.28853303]. Source data are provided with this paper.

The custom code used for RNA-seq, ATAC-seq, and Ca²⁺ imaging analyses in this study is publicly available on GitHub [https://github.com/tmurano/REPeatedOPtogeneticStimulation] and is archived on Zenodo with a citable DOI (https://doi.org/10.5281/zenodo.19904295).

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 9 MeSH terms, 1 funder, 132 references, 14 RRIDs.

Cite

This paper

Murano, T., Hagihara, H., Tajinda, K., Takao, K., Takamiya, Y., Katoh, K., Robison, A. J., Matsumoto, M., Namihira, M., & Miyakawa, T. (2026). Repetitive neuronal activation regulates cellular maturation state via nuclear reprogramming. Nature communications, 17(1), 5881. https://doi.org/10.1038/s41467-026-74202-w

BibTeX

@article{murano2026repetitive,
author = {Murano, Tomoyuki and Hagihara, Hideo and Tajinda, Katsunori and Takao, Keizo and Takamiya, Yoshihiro and Katoh, Kaoru and Robison, Alfred J and Matsumoto, Mitsuyuki and Namihira, Masakazu and Miyakawa, Tsuyoshi},
title = {{Repetitive neuronal activation regulates cellular maturation state via nuclear reprogramming}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {5881},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74202-w},
url = {https://doi.org/10.1038/s41467-026-74202-w},
pmid = {42469214},
pmcid = {PMC13379389}
}

RIS

TY - JOUR
AU - Murano, Tomoyuki
AU - Hagihara, Hideo
AU - Tajinda, Katsunori
AU - Takao, Keizo
AU - Takamiya, Yoshihiro
AU - Katoh, Kaoru
AU - Robison, Alfred J
AU - Matsumoto, Mitsuyuki
AU - Namihira, Masakazu
AU - Miyakawa, Tsuyoshi
TI - Repetitive neuronal activation regulates cellular maturation state via nuclear reprogramming
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/17
VL - 17
IS - 1
SP - 5881
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74202-w
UR - https://doi.org/10.1038/s41467-026-74202-w
LA - en
ER -

CSL-JSON

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