OSCR

Human brain organoids record the passage of time over multiple years.

Code ↔ Paper

21 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 21 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › scRNA-seq data analysis › Cluster annotation and data integration ↔ Harmony.R, the whole file · a weak match · score 0.96 · PrepSCTFindMarkers, SelectIntegrationFeatures, RunPCA, median UMI, repeatedly variable, variable features
  2. [2] § Methods › scRNA-seq data analysis › Analysis of presynapse, synapse and post-synapse signature modules ↔ SynGO_Signatures_ModuleScores.R, lines 23–66 · score 0.93 · signature modules, AddModuleScore, SynGO, module scores, scRNA, retrieved
  3. [3] § Methods › scRNA-seq data analysis › scRNA-seq data quality control, normalization, dimensionality reduction and clustering ↔ Harmony.R, the whole file · a weak match · score 0.87 · FindClusters, FindNeighbors, RunPCA, SCTransform, Seurat, neighbour
  4. [4] § Methods › DNA extraction and methylome analyses › Genomic DNA extraction and WGBS › Epigenetic clocks ↔ agePrediction_methylclocks.R, lines 2–40 · score 0.83 · epic hg19 manifest, methylation clock, DNA methylation, methylclock, boostme, Imputation
  5. [5] § Methods › DNA extraction and methylome analyses › Genomic DNA extraction and WGBS › WGBS processing ↔ meanMethylation_DMV_PMD_HMD.sh, the whole file · a weak match · score 0.77 · UCSC tools, cdDMRs, bigWigAverageOverBed, DMVs, bins, resolution
  6. [6] § Methods › DNA extraction and methylome analyses › Genomic DNA extraction and WGBS › Epigenetic clocks ↔ imputation_boostMe.R, the whole file · a weak match · score 0.75 · low coverage, methylation clock, Imputation, bsseq, boostme, methylclock
  7. [7] § Methods › Electrophysiological analyses ↔ Electrophysiological_Statistics_FigureGeneration.R, lines 1–41 · score 0.75 · burst rate, spiking rate, amplitude, Prism, duration, MEA
  8. [8] § Cortical organoids age appropriately in culture ↔ PredCorticalAge/CorticalClock.r, lines 138–212 · score 0.72 · DNA methylation age, cortical clock, human tissue, chronological age, Pearson, error
  9. [9] § Methods › scRNA-seq data analysis › Comparative analysis of DIALOGUE-determined maturation scores ↔ SynGO_Signatures_ModuleScores.R, lines 118–185 · score 0.67 · module scores, weight factor, lme4, anova, genotype, model
  10. [10] § Methods › DNA extraction and methylome analyses › Genomic DNA extraction and WGBS › WGBS processing ↔ endogenousRef_cdDMRs.R, lines 1–54 · score 0.67 · UCSC tools, cdDMRs, bigWigAverageOverBed, WGBS, methylation, brain
  11. [11] § Methods › scRNA-seq data analysis › Cluster annotation and data integration ↔ CellType_Proportions_Change_FigureGeneration.R, lines 11–52 · score 0.64 · glial precursors, scRNA, OPCs, immature, CPNs, astrocytes
  12. [12] § Cortical organoids age appropriately in culture ↔ agePrediction_methylclocks.R, lines 2–40 · score 0.63 · DNA methylation clock, fetal brain clock, epigenetic clock, Horvath, predicted, Cortical
  13. [13] § Methods › scRNA-seq data analysis › Comparing cell type proportions between treatment conditions ↔ CellType_Proportions_Change_FigureGeneration.R, lines 54–106 · score 0.61 · glmer.nb, offset, NBME, anova, genotypes, treatment
  14. [14] § APM medium maintains excitatory neurons ↔ CellType_Proportions_Change_FigureGeneration.R, lines 108–172 · score 0.59 · glial precursors, NBME, expanded, OPCs, immature, composition
  15. [15] § Cortical organoids age appropriately in culture ↔ PredCorticalAge/CorticalClock.r, lines 138–212 · score 0.59 · DNA methylation age, DNAm age, error, median, correlating, tissue
  16. [16] § Methods › DNA extraction and methylome analyses › Genomic DNA extraction and WGBS › Visualizations ↔ meanMethylation_DMV_PMD_HMD.sh, the whole file · a weak match · score 0.57 · UCSC tools, bigWigAverageOverBed, bins, hg19, methylation
  17. [17] § APM medium maintains excitatory neurons ↔ CellType_Proportions_Change_FigureGeneration.R, lines 108–172 · score 0.56 · ChPL, UMAP, precursors, immature, CPNs, bars
  18. [18] § Methods › DNA extraction and methylome analyses › Genomic DNA extraction and WGBS › Visualizations ↔ endogenousRef_cdDMRs.R, lines 1–54 · score 0.55 · UCSC tools, bigWigAverageOverBed, DMR, methylation, month
  19. [19] § APM medium maintains excitatory neurons ↔ SynGO_Signatures_ModuleScores.R, lines 68–116 · score 0.55 · likelihood ratio, module score, linear, models, neurons, organoids
  20. [20] § APM medium maintains excitatory neurons ↔ SynGO_Signatures_ModuleScores.R, lines 68–116 · score 0.55 · linear mixed, module scores, populations, progenitors, model, neurons
  21. [21] § Methods › scRNA-seq data analysis › Analysis of previously published human fetal data ↔ CellType_Proportions_Change_FigureGeneration.R, lines 11–52 · score 0.53 · RNA seq, metadata, OPCs, subsetted, glial, astrocytes

Paper

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The authors' code

R · 215 lines · 9.6 KB · no license · 5 matches

  1. require(lme4)
  3. # convert significance values into asterisks
  4. makeAsterisk <- function(x){
  5. stars <- c("****", "***", "**", "*", "ns")
  6. vec <- c(0, 0.0001, 0.001, 0.01, 0.05, 1)
  7. i <- findInterval(x, vec)
  8. stars[i]
  9. }
  11. media_cols <- c(
  12. 'CDM4' = '#00B8A9',
  13. 'Bphys' = '#F6416C',
  14. 'APM' = '#F6416C'
  15. )
  17. umap.colors <- c(
  18. 'aRG' = "#8dd3c7",
  19. 'oRG' = "#f9e79d",
  20. 'IP'= "#bebada",
  21. 'PN' = "#fcbba1",
  22. 'IN progenitors' = "#F0C4D0",
  23. 'CFuPN' = "#80b1d3",
  24. 'CPN' = "#fdb462",
  25. 'Immature IN' = "#fa9fb5",
  26. 'tRG' = "#9BD19B",
  27. 'Glial precursors' = "#43A85E",
  28. 'Astrocytes' = "#005A40",
  29. 'OPC' = "#c3c483",
  30. 'ChPL' = "#e1ce9a"
  31. )
  33. # use NBEM method to evaluate cell type proportional changes ----
  34. # load meta data of age-specific integrated object from CDM4 & APM (BrainPhys/Bphys-based) scRNA-seq data (i.e. 6mo)
  35. meta.6mo <- readRDS("6mo_harmonized_Metadata.RDS")
  37. # generate contingency table of: Organoid x BroadType
  38. myTab.all <- unclass(table(meta.6mo$Organoid, meta.6mo$BroadType))
  39. myTab.all <- data.frame(myTab.all)
  40. colnames(myTab.all) <- str_replace_all(colnames(myTab.all), "\\.", " ")
  41. CellType.all <- colnames(myTab.all)
  43. # Use the total cell count per organoid as an offset; add columns of treatment and genotype from sample ids
  44. myTab.all$LibSize <- apply(myTab.all, 1, sum)
  45. myTab.all$Treatment <- factor(sapply(strsplit(rownames(myTab.all), "_"), "[", 1), levels = c("CDM4", "Bphys"))
  46. myTab.all$Genotype <- factor(sapply(strsplit(rownames(myTab.all), "_"), "[", 4))
  48. # subset based on the shared genotypes between CDM4 and APM scRNA-seq data
  49. genotype.tb <- table(myTab.all$Treatment, myTab.all$Genotype)
  50. shared_genotypes <- colnames(genotype.tb)[apply(genotype.tb, 2, function(x) all(x > 0))]
  51. myTab <- myTab.all[myTab.all$Genotype %in% shared_genotypes,]
  52. myTab <- droplevels(myTab)
  54. # create output directory
  55. date <- str_replace_all(Sys.Date(), "-", "")
  56. outDir <- paste0("visualization_df/CellTypeProp/", date, "/")
  57. if(!dir.exists(outDir)){
  58. dir.create(outDir, recursive = TRUE)
  59. }
  61. Bphys.stats <- data.frame(CellType = character(),
  62. pvalue = numeric())
  64. print("Bphys vs. CDM4:")
  65. anova.res <- list()
  66. RandMod1 <- list()
  67. TreatMod1 <- list()
  68. for (i in 1:length(CellType.all)) {
  69. print(CellType.all[i])
  70. myTab$TypeOfInterest <- myTab[[CellType.all[i]]]
  72. # if the cell type is found in both conditions, proceed
  73. if (all(table(myTab$TypeOfInterest > 0, myTab$Treatment)['TRUE', ] > 0) == TRUE) {
  74. # if any sample has zero cell count in TypeOfInterest column, add small constant 1 to every TypeOfInterest. Each LibSize will increment by 1 accordingly
  75. if (any(myTab$TypeOfInterest == 0)) {
  76. myTab$TypeOfInterest <- myTab$TypeOfInterest + 1
  77. myTab$LibSize <- myTab$LibSize + 1
  78. }
  79. # First attempt at fitting the RandomModel and TreatmentModel
  80. tryCatch({
  81. RandomModel <- glmer.nb(TypeOfInterest ~ offset(log(LibSize)) + (1|Genotype), data=myTab)
  82. TreatmentModel <- glmer.nb(TypeOfInterest ~ Treatment + offset(log(LibSize)) + (1|Genotype), data=myTab)
  83. }, error = function(e) {
  84. # If an error occurs, define control parameters and try fitting again
  85. cat("Encountered error. Refitting models with custom control parameters...\n")
  86. control_params <- glmerControl(optCtrl = list(maxfun = 10000), check.conv.grad = .makeCC("warning", tol = 3e-3))
  88. RandomModel <- glmer.nb(TypeOfInterest ~ offset(log(LibSize)) + (1|Genotype), data = myTab, control = control_params)
  89. TreatmentModel <- glmer.nb(TypeOfInterest ~ Treatment + offset(log(LibSize)) + (1|Genotype), data = myTab, control = control_params)
  90. })
  92. anova.res[[CellType.all[i]]] <- anova(RandomModel, TreatmentModel)
  93. Bphys.stats[i, ] <- c(CellType.all[i], scientific(anova.res[[CellType.all[i]]][2, 8]))
  94. RandMod1[[CellType.all[i]]] <- RandomModel
  95. TreatMod1[[CellType.all[i]]] <- TreatmentModel
  96. } else {
  97. print(paste0(CellType.all[[i]], " is only found in one condition, skip for testing.\n"))
  98. Bphys.stats[i, ] <- c(CellType.all[i], NA)
  99. next
  100. }
  101. }
  102. Bphys.stats$p.adjust <- scientific(p.adjust(Bphys.stats$pvalue, method="BH"))
  103. Bphys.stats$asterisk <- makeAsterisk(Bphys.stats$p.adjust)
  104. Bphys.stats
  106. save(RandMod1, TreatMod1, anova.res, Bphys.stats, file = paste0(outDir, "6mo_NBME_Bphys_vs_CDM4.RData"))
  108. # figure generation ----
  109. df.all <- table(droplevels(meta.6mo$Organoid[meta.6mo$Genotype %in% shared_genotypes]),
  110. droplevels(meta.6mo$BroadType[meta.6mo$Genotype %in% shared_genotypes]))
  111. df.all <- (df.all / apply(df.all, 1, sum)) * 100
  112. df.all <- reshape2::melt(df.all)
  113. colnames(df.all) <- c("Organoid", "Cell Type", "Percent of Cells")
  114. df.all$Treatment <- sapply(strsplit(as.character(df.all$Organoid), "_"), "[", 1)
  115. df.all$Treatment <- factor(df.all$Treatment, levels = c("CDM4", "Bphys"))
  116. df.all
  117. glimpse(df.all)
  119. # assign broader categories to cell types for visual arrangements
  120. df.all$category <- ""
  121. df.all$category[df.all$`Cell Type` %in% c("aRG", "oRG", "IP", "PN", "IN progenitors")] <- "Category 1"
  122. df.all$category[df.all$`Cell Type` %in% c("CFuPN", "CPN", "Immature IN")] <- "Category 2"
  123. df.all$category[df.all$`Cell Type` %in% c("tRG", "Glial precursors", "Astrocytes", "OPC", "ChPL")] <- "Category 3"
  125. df.all$`Cell Type` <- factor(df.all$`Cell Type`, levels = c("aRG", "oRG", "IP", "PN", "IN progenitors",
  126. "CFuPN", "CPN", "Immature IN",
  127. "tRG", "Glial precursors", "Astrocytes", "OPC", "ChPL"))
  129. mean_df.all <- df.all %>%
  130. group_by(category, `Cell Type`, Treatment) %>%
  131. dplyr::summarise(mean= mean(`Percent of Cells`),
  132. se = sd(`Percent of Cells`)/sqrt(n()))
  133. mean_df.all
  134. save(RandMod1, TreatMod1, Bphys.stats, df.all, mean_df.all, file = paste0(outDir, "6mo_NBME_Bphys_vs_CDM4.RData"))
  136. # load statistics
  137. stats1 <- Bphys.stats
  139. # iterate and generate sub-barplots for each category
  140. barplots <- list()
  141. for (case in unique(mean_df.all$category)) {
  142. df <- df.all[df.all$category == case,]
  143. df$`Cell Type` <- droplevels(df$`Cell Type`)
  145. mean_df <- mean_df.all[mean_df.all$category == case,]
  146. mean_df$`Cell Type` <- droplevels(mean_df$`Cell Type`)
  148. strip <- strip_themed(background_x = elem_list_rect(fill = umap.colors[levels(mean_df$`Cell Type`)]),
  149. text_x = elem_list_text(colour = c("white"), face = "bold", size = 10))
  151. composition.barplot <- ggplot(mean_df) +
  152. geom_bar(aes(x = `Treatment`, y = mean, fill = `Treatment`), stat="identity", alpha=0.8) +
  153. scale_fill_manual(values = media_cols) +
  154. geom_errorbar( aes(x = `Treatment`, ymin=mean-se, ymax=mean+se), width=0.2, colour="#6C6C6C", linewidth=.5) +
  155. geom_point(aes(x = `Treatment`, y = `Percent of Cells`, color = `Treatment`), size = 1.5, alpha=0.8, data = df, position = position_jitter(seed = 42,width=0.15, height=0)) +
  156. scale_color_manual(values = media_cols) +
  157. facet_grid2(. ~ `Cell Type`, strip = strip, scales="free_x") +
  158. theme_classic() +
  159. theme(axis.text.x = element_blank(),
  160. axis.title.x = element_blank(),
  161. axis.ticks=element_blank(),
  162. axis.ticks.x.bottom = element_blank(),
  163. axis.title.y = element_text(size = 12),
  164. panel.grid.major = element_blank(),
  165. strip.background = element_rect(colour=NA, fill=NA),
  166. panel.spacing.x = unit(0, "null"),
  167. panel.border = element_rect(fill = NA, color = "white"),
  168. legend.title = element_text(size=13), #change legend title font size
  169. legend.text = element_text(size=12)) +
  170. labs(y = "Percent of Cells") +
  171. guides(color = "none") +
  172. scale_y_continuous(expand = c(0, 0), limits = c(0, max(df$`Percent of Cells`) + 10))
  174. # for adding significance notations on grouped barplots at corresponding positions;
  175. # one way is to replace corresponding statistical values from a Wilcox_test tibble (used for anchor positions of stats) with the statistics
  176. stats.pos <- df %>%
  177. group_by(`Cell Type`) %>%
  178. wilcox_test(`Percent of Cells` ~ `Treatment`, ref.group = "CDM4") %>%
  179. adjust_pvalue(method = "BH") %>%
  180. add_significance() %>%
  181. add_xy_position(x = "Treatment")
  183. for (col in c("statistic", "p", "p.adj", "p.adj.signif")) {
  184. stats.pos[[col]] <- NULL
  185. }
  187. stats.pos <- left_join(stats.pos,
  188. stats1, by = c('Cell Type' = 'CellType'))
  189. stats.pos$`Cell Type` <- factor(stats.pos$`Cell Type`,
  190. levels = levels(mean_df$`Cell Type`))
  191. stats.pos$y.position <- stats.pos$y.position - 1
  193. composition.barplot <- composition.barplot + stat_pvalue_manual(stats.pos[stats.pos$asterisk != "ns",],
  194. hide.ns = TRUE,
  195. label = "{asterisk}",
  196. label.size = 8,
  197. bracket.nudge.y = -4,
  198. remove.bracket = T)
  199. legend <- get_legend(composition.barplot)
  200. composition.barplot <- composition.barplot + NoLegend()
  202. barplots[[case]] <- composition.barplot
  203. }
  205. barplots.grid <- plot_grid(plotlist = barplots, ncol = 3, rel_widths = c(.5, .3, .5))
  206. combined.barplot <- plot_grid(barplots.grid, legend, ncol = 2, rel_widths = c(1, .1))
  207. combined.barplot
  209. # save the plot in svg format
  210. ggsave(paste0("6mo_CellType_compositions_comparison_barplots_Bphys_vs_CDM4_with_signif_", str_replace_all(Sys.Date(), "-", ""), ".svg"), combined.barplot, path = outDir, width = 16.5, height = 4.4)

CellType_Proportions_Change_FigureGeneration.R at commit fbf7b57, no license · at the source

Overview

Authors: Irene Faravelli1,2,3, Noelia Antón-Bolaños1,2,4, Anqi Wei1,2, Tyler Faits1,2, Abhishek Sampath Kumar1,2, Sophia Andreadis1,2, Rahel Kastli1,2, Marta Montero Crespo1,2, Mara Steiger5,6, Daniel Leible7, Elizabeth Zhang7, Bobae An7, Yaron Meirovitch8, Sayara Silwal1, Sung Min Yang1,2, Alexander Kovacsovics5, Xian Adiconis2,9, Helene Kretzmer5,6, Joshua Z. Levin2,9, Edward S. Boyden7, Jeff Lichtman8, Aviv Regev9,10, Alexander Meissner5,11, Paola Arlotta1,2
ORCID iDs: Noelia Antón-Bolaños, Tyler Faits, Abhishek Sampath Kumar, Rahel Kastli, Marta Montero Crespo, Mara Steiger, Daniel Leible, Elizabeth Zhang, Helene Kretzmer, Joshua Z. Levin, Edward S. Boyden, Jeff Lichtman, Alexander Meissner, Paola Arlotta
  1. Department of Stem Cell & Regenerative Biology, Harvard University,Cambridge, MA USA
  2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard,Cambridge, MA USA
  3. Present Address: Department for Clinical Sciences and Community Health (DISCCO), University of Milan,Milan, Italy
  4. Present Address: Department of Translational Neuroscience, University Medical Center Utrecht (UMCU), Brain Center, Utrecht University,Utrecht, The Netherlands
  5. Department of Genome Regulation, Max Planck Institute for Molecular Genetics,Berlin, Germany
  6. Digital Health Cluster, Hasso Plattner Institute for Digital Engineering, Digital Engineering Faculty, University of Potdsdam,Potsdam, Germany
  7. McGovern Institute, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology,Cambridge, MA USA
  8. Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University,Cambridge, MA USA
  9. Klarman Cell Observatory, Broad Institute of MIT and Harvard,Cambridge, MA USA
  10. Genentech,San Francisco, CA USA
  11. Broad Institute of MIT and Harvard,Boston, MA USA
Institutions: Harvard University (United States); Broad Institute (United States); University of Milan (Italy); University Medical Center Utrecht (Netherlands); Max Planck Institute for Molecular Genetics (Germany); Hasso Plattner Institute (Germany); Massachusetts Institute of Technology (United States); Genentech,San Francisco, CA USA
Journal: Nature, volume 657, issue 8132, pages 713-723
Dates: received 2 July 2025; accepted 1 July 2026; published online 19 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41586-026-10877-x · PMID 42618795 · PMCID PMC13581598 · OpenAlex W7203754433
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), developmental (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuronal development, Cellular neuroscience
MeSH: Aging*, Brain*, Organoids*, Animals, DNA Methylation, Epigenomics, Female, Humans, Male, Neural Stem Cells, Neurons, Time Factors (* major topic)
Topic: Pluripotent Stem Cells Research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: cited by 3 papers (Europe PMC); 75 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.

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LSteg/EpigeneticFetalClock

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Commit: 42da54253d1291ac5aa3baf11daab4431b13568a, 5 May 2021
Languages: R (2)
Size: 6 files, 2 scripts
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Found in: the text, “Endogenous comparison”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), ggplot2 (1 file), ggpubr (1 file), glmnet (1 file), lme4 (1 file), lmerTest (1 file), reshape2 (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/LSteg/EpigeneticFetalClock

gemmashireby/CorticalClock

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Commit: 80c3df19c01d9aac25c9fd5aecd5bbc42aba0939, 26 March 2021
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Size: 7 files, 2 scripts
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Found in: the text, “Epigenetic clocks”
Holds: README
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Tools: glmnet (1 file)
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At the source: github.com/gemmashireby/CorticalClock

tfaits/Arlotta_Lab_LongTermOrganoids

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Commit: fbf7b574fbe77fc8309f45eaa1bcff91e0075261, 3 June 2026
Languages: R (7), Shell (2)
Size: 9 files, 9 scripts
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Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), ggplot2 (3 files), data.table (2 files), lme4 (2 files), reshape2 (2 files), rstatix (2 files), Seurat (2 files), BEDTools (1 file), ggpubr (1 file), Harmony (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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9 files
At the source: github.com/tfaits/Arlotta_Lab_LongTermOrganoids

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

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Recorded: type, language, journal, volume, issue, pages, dates, 24 authors, 2 keywords, 12 MeSH terms, 74 references.

Cite

This paper

Faravelli, I., Antón-Bolaños, N., Wei, A., Faits, T., Kumar, A. S., Andreadis, S., Kastli, R., Crespo, M. M., Steiger, M., Leible, D., Zhang, E., An, B., Meirovitch, Y., Silwal, S., Yang, S. M., Kovacsovics, A., Adiconis, X., Kretzmer, H., Levin, J. Z., . . . Arlotta, P. (2026). Human brain organoids record the passage of time over multiple years. Nature, 657(8132), 713-723. https://doi.org/10.1038/s41586-026-10877-x

BibTeX

@article{faravelli2026human,
author = {Faravelli, Irene and Antón-Bolaños, Noelia and Wei, Anqi and Faits, Tyler and Kumar, Abhishek Sampath and Andreadis, Sophia and Kastli, Rahel and Crespo, Marta Montero and Steiger, Mara and Leible, Daniel and Zhang, Elizabeth and An, Bobae and Meirovitch, Yaron and Silwal, Sayara and Yang, Sung Min and Kovacsovics, Alexander and Adiconis, Xian and Kretzmer, Helene and Levin, Joshua Z. and Boyden, Edward S. and Lichtman, Jeff and Regev, Aviv and Meissner, Alexander and Arlotta, Paola},
title = {{Human brain organoids record the passage of time over multiple years}},
journal = {Nature},
year = {2026},
month = aug,
volume = {657},
number = {8132},
pages = {713--723},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10877-x},
url = {https://doi.org/10.1038/s41586-026-10877-x},
pmid = {42618795},
pmcid = {PMC13581598}
}

RIS

TY - JOUR
AU - Faravelli, Irene
AU - Antón-Bolaños, Noelia
AU - Wei, Anqi
AU - Faits, Tyler
AU - Kumar, Abhishek Sampath
AU - Andreadis, Sophia
AU - Kastli, Rahel
AU - Crespo, Marta Montero
AU - Steiger, Mara
AU - Leible, Daniel
AU - Zhang, Elizabeth
AU - An, Bobae
AU - Meirovitch, Yaron
AU - Silwal, Sayara
AU - Yang, Sung Min
AU - Kovacsovics, Alexander
AU - Adiconis, Xian
AU - Kretzmer, Helene
AU - Levin, Joshua Z.
AU - Boyden, Edward S.
AU - Lichtman, Jeff
AU - Regev, Aviv
AU - Meissner, Alexander
AU - Arlotta, Paola
TI - Human brain organoids record the passage of time over multiple years
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/08/19
VL - 657
IS - 8132
SP - 713
EP - 723
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10877-x
UR - https://doi.org/10.1038/s41586-026-10877-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41586-026-10877-x",
"type": "article-journal",
"title": "Human brain organoids record the passage of time over multiple years",
"container-title": "Nature",
"author": [
{
"family": "Faravelli",
"given": "Irene"
},
{
"family": "Antón-Bolaños",
"given": "Noelia"
},
{
"family": "Wei",
"given": "Anqi"
},
{
"family": "Faits",
"given": "Tyler"
},
{
"family": "Kumar",
"given": "Abhishek Sampath"
},
{
"family": "Andreadis",
"given": "Sophia"
},
{
"family": "Kastli",
"given": "Rahel"
},
{
"family": "Crespo",
"given": "Marta Montero"
},
{
"family": "Steiger",
"given": "Mara"
},
{
"family": "Leible",
"given": "Daniel"
},
{
"family": "Zhang",
"given": "Elizabeth"
},
{
"family": "An",
"given": "Bobae"
},
{
"family": "Meirovitch",
"given": "Yaron"
},
{
"family": "Silwal",
"given": "Sayara"
},
{
"family": "Yang",
"given": "Sung Min"
},
{
"family": "Kovacsovics",
"given": "Alexander"
},
{
"family": "Adiconis",
"given": "Xian"
},
{
"family": "Kretzmer",
"given": "Helene"
},
{
"family": "Levin",
"given": "Joshua Z."
},
{
"family": "Boyden",
"given": "Edward S."
},
{
"family": "Lichtman",
"given": "Jeff"
},
{
"family": "Regev",
"given": "Aviv"
},
{
"family": "Meissner",
"given": "Alexander"
},
{
"family": "Arlotta",
"given": "Paola"
}
],
"container-title-short": "Nature",
"volume": "657",
"issue": "8132",
"page": "713-723",
"DOI": "10.1038/s41586-026-10877-x",
"PMID": "42618795",
"PMCID": "PMC13581598",
"ISSN": "0028-0836",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41586-026-10877-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
19
]
]
}
}

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