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GRASPing experience-dependent protein expression signatures enriched for hippocampal engram cell synapses.

Code ↔ Paper

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The 4 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › MS data analysis ↔ MSDAP.zip/MSDAP/MSDAP_analysis_filtered_egrasp_peptides.R, the whole file · a weak match · score 0.89 · missed cleavage, MS DAP, cleaver, digestion, silico, trypsin
  2. [2] § MATERIALS AND METHODS › MS data analysis ↔ MSDAP.zip/MSDAP/MSDAP_analysis_original.R, the whole file · a weak match · score 0.89 · DIA NN, Mouse ID, MS DAP, FASTA, algorithm, MSqRob
  3. [3] § MATERIALS AND METHODS › Statistics for spine morphometric analysis ↔ structural_dendrite_spine_analysis.zip/structural_dendrite_spine_analysis/SpineParameter_Analysis+Example.R, lines 237–286 · score 0.84 · fixed intercept model, random intercept model, predictor model, fitted, linearity, residuals
  4. [4] § MATERIALS AND METHODS › CA3T-CA1E/CA3E-CA1E overlap, morphometry, and Grm5 expression/localization analysis ↔ structural_dendrite_spine_analysis.zip/structural_dendrite_spine_analysis/SpineParameter_Analysis+Example.R, lines 237–286 · score 0.51 · post hoc, pairwise, models, animal, Dendrites, spines

Paper

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The authors' code

R · 287 lines · 11 KB · CC-BY-4.0 · 2 matches

  1. ####### Script to identify spine clusters and spine types #######
  2. ## 0. required packages
  3. ## 1. spine morphology - spine types + normalisation
  4. ## 2. spine number & distribution - cluster + normalisation
  5. ## 3. example genearlised linear model taking nested data structure into account
  8. #####
  9. ### 0. required packages
  10. install.packages("readxl", dependencies = TRUE)
  11. install.packages("tidyverse", dependencies = TRUE)
  13. install.packages("reshape2", dependecies = TRUE)
  14. install.packages("dplyr", dependencies = TRUE)
  15. install.packages("generics", dependencies = TRUE)
  17. install.packages("lme4", dependencies = TRUE)
  18. install.packages("nlme", dependencies = TRUE)
  19. install.packages("emmeans", dependencies = TRUE)
  21. install.packages("scatterplot3d")
  22. install.packages("cluster")
  23. install.packages("factoextra")
  24. install.packages("corrplot")
  26. # Load packages
  27. library(tidyverse)
  28. library(readxl)
  30. library(reshape2)
  31. library(dplyr)
  32. library(generics)
  34. library(lme4) # recommeneded to run generalised linear models with nested data structure
  35. library(nlme) #nonlinear mixed-effects models
  36. library(emmeans)
  38. library(scatterplot3d) # making 3D scatter plot plot
  39. library(cluster) # clustering algorithms
  40. library(factoextra) # clustering algorithms & visualization
  41. library(corrplot) # making correlation plots for PCA analysis
  43. rm(list = ls())
  44. graphics.off()
  47. # there are two independent variables in this data set reactivation status and condition
  48. # condition (CFC vs CE)
  49. # reactivation status (cFos+RFP+ vs cFos-RFP+)
  50. # !please adapt code to your experimental design!
  52. #adust working directory to location in which data files are stored
  53. setwd("~/Documents/Data_Compiler_Example/Processed_data/Output")
  56. #####
  57. ### 1. spine morphology - spine types + normalisation
  58. rm(list = ls())
  59. graphics.off()
  61. # read data file
  62. df <- read_excel("morph_data.xlsx")
  64. df <- rename(df, spine_mean_head = `Spine Part Mean Diameter Head`)
  65. df <- rename(df, spine_max_head = `Spine Part Max Diameter Head`)
  66. df <- rename(df, spine_volume_head = `Spine Part Volume Head`)
  67. df <- rename(df, spine_mean_neck = `Spine Part Mean Diameter Neck`)
  68. df <- rename(df, spine_max_neck = `Spine Part Max Diameter Neck`)
  69. df <- rename(df, spine_volume_neck = `Spine Part Volume Neck`)
  70. df <- rename(df, spine_length = `Spine Length`)
  71. df <- rename(df, spine_volume = `Spine Volume`)
  73. # create new variable based on ratio of spine head and neck volume
  74. df$head_neck_ratio <- df$spine_volume_head / df$spine_volume_neck
  76. # create empty vector for for loop
  77. df$spine_type <- vector(mode = "numeric", length = length(df$spine_length))
  79. # classify each spine as stubby/mushroom/thin/unknown type based on specified criteria
  80. for (i in 1:length(df$spine_length)) {
  81. if ((df$spine_length[i] <= 1 & df$head_neck_ratio[i] < 1.2) | df$spine_max_neck[i] == 0) {
  82. df$spine_type[i] <- "stubby"
  83. } else if (df$spine_length[i] <= 5 & df$head_neck_ratio[i] >= 1.2) {
  84. df$spine_type[i] <- "mushroom"
  85. } else if (df$spine_length[i] <= 5 & df$head_neck_ratio[i] < 1.2) {
  86. df$spine_type[i] <- "thin"
  87. } else {
  88. df$spine_type[i] <- "unknown"
  89. }
  90. }
  92. # factorise variable for later analyses
  93. df$spine_type <- as.factor(df$spine_type)
  96. # counts the number of spines on each dendrite
  97. n_spines_dendrite <- df %>% count(dendrite, sort = TRUE)
  98. names(n_spines_dendrite)[names(n_spines_dendrite) == "n"] <- "n_spines_type"
  99. df <- merge(df, n_spines_dendrite, by = c("dendrite"), all = TRUE)
  101. # counts the number of spine types on each dendrite
  102. df1 <- df %>%
  103. group_by(animal, image, dendrite, condition, reactivation_status, n_spines_type) %>%
  104. count(spine_type)
  105. names(df1)[names(df1) == "n"] <- "frequency"
  107. # calculates the ratio of each spine type to the total amount of spines on each dendrite
  108. df1$relative_frequ <- df1$frequency / df1$n_spines_type
  111. #####
  112. ### 2. spine number & distribution - cluster + normalisation
  113. ## sort data according to dendrite ID and to distance from dendrite beginning point
  114. rm(list = ls())
  115. graphics.off()
  117. # read data file
  118. df <- read_excel("distr_dendrite_data.xlsx")
  120. df <- rename(df, spine_attachment = `Spine Attachment Pt Distance`)
  121. df <- rename(df, spine_attachment_diameter = `Spine Attachment Pt Diameter`)
  122. df <- rename(df, spine_density = `Dendrite Spine Density`)
  123. df <- rename(df, dendrite_length = `Dendrite Length`)
  125. df <- df[
  126. with(df, order(dendrite, spine_attachment)),
  127. ]
  130. ## calculate interspine distance by calculating distance between spine and spine before in df
  131. df$lag_spine_attachment <- dplyr::lag(df$spine_attachment, n = 1) # creates new variable with spine attachment ahead of spine
  132. df$interspine_distance <- df$spine_attachment - df$lag_spine_attachment # calculates interspine distance between current spine and spine before
  133. df$interspine_distance <- ifelse(df$interspine_distance < 0, NA, df$interspine_distance) #if distance is negative, it must be a previous dendrite -> set distance to NA
  135. ## calculate interspine distance to two spines before spine in df
  136. df$lag_spine_attachment2 <- dplyr::lag(df$spine_attachment, n = 2) # creates new variable with spine attachment two spines ahead of spine
  138. df$distance_to_twoneighbours_before <- (df$spine_attachment - df$lag_spine_attachment2) # calculates interspine distance between spine and TWO spines before
  140. df$cluster <- ifelse(df$distance_to_twoneighbours_before <= 1.5 & df$distance_to_twoneighbours_before >= 0, 1, 0) # if interspine distance between spine and two spines before is larger than 0 and smaller than 1.5, these three spines are defined as a cluster, otherwise not
  141. df$cluster[is.na(df$cluster)] <- 0
  143. df$cluster_before <- dplyr::lag(df$cluster, n = 1) # creates new variable with cluster information before current spine
  144. df$cluster_before[is.na(df$cluster_before)] <- 0
  146. df_omit <- na.omit(df)
  149. ###cluster
  150. count_overall <- 0
  151. count_size <- 0
  152. i <- 0
  154. ## total number of clusters
  155. # counts the total amount of clusters across the entire df
  156. for (i in 1:length(df_omit$cluster)) {
  157. if (df_omit$cluster[i] == 1 & df_omit$cluster_before[i] == 0) {
  158. count_overall <- count_overall + 1
  159. } else {
  160. count_overall <- count_overall
  161. }
  162. }
  164. ## size of all clusters together
  165. #
  166. for (i in 1:length(df_omit$cluster)) {
  167. if (df_omit$cluster[i] == 1 & df_omit$cluster_before[i] == 0) {
  168. count_overall <- count_overall + 1
  169. count_size <- count_size + 1
  170. } else if ((df_omit$cluster[i] == 1 & df_omit$cluster_before[i] == 1)) {
  171. count_overall <- count_overall
  172. count_size <- count_size + 1
  173. } else {
  174. count_overall <- count_overall
  175. count_size <- 0
  176. }
  177. df_omit$count_size[i] <- count_size
  178. }
  180. ## size of each cluster
  181. local_maxima <- function(x) {
  182. # Use -Inf instead if x is numeric (non-integer)
  183. y <- diff(c(-.Machine$integer.max, x)) > 0L
  184. rle(y)$lengths
  185. y <- cumsum(rle(y)$lengths)
  186. y <- y[seq.int(1L, length(y), 2L)]
  187. if (x[[1]] == x[[2]]) {
  188. y <- y[-1]
  189. }
  190. y
  191. }
  194. df_omit$maxima <- 0
  195. df_omit$maxima[local_maxima(df_omit$count_size)] <- df_omit$count_size[local_maxima(df_omit$count_size)] ## extracts the largest number of spines within a cluster, to determine size of cluster
  196. df_omit$cluster_size <- ifelse(df_omit$maxima > 0, df_omit$maxima + 2, 0) # each cluster contains of 3 spines, therefore cluster size is 1 + 2
  199. # counts the number of spines on each dendrite
  200. n_spines_dendrite <- df_omit %>% count(dendrite, sort = TRUE)
  201. names(n_spines_dendrite)[names(n_spines_dendrite) == "n"] <- "n_spines_cluster"
  202. df_omit <- merge(df_omit, n_spines_dendrite, by = c("dendrite"), all = TRUE)
  205. #counts the number of clusters consisting of 3 spines across each dendrite
  206. df2 <- df_omit %>%
  207. group_by(animal, image, dendrite, condition, reactivation_status, n_spines_cluster) %>%
  208. count(cluster)
  209. names(df2)[names(df2) == "n"] <- "frequency"
  211. df2$animal <- as.factor(df2$animal)
  212. df2$cluster <- as.factor(df2$cluster)
  214. # adding correct lables to cluster variable
  215. levels(df2$cluster)[levels(df2$cluster)=="0"] <- "no cluster"
  216. levels(df2$cluster)[levels(df2$cluster)=="1"] <- "cluster"
  218. # counts the number of clustered and non-clustered spines along each dendrite
  219. df2_w <- dcast(df2, animal + dendrite + image + condition + reactivation_status + n_spines_cluster ~ cluster, value.var = "frequency")
  220. df2_w <- df2_w %>% replace(is.na(df2_w), 0)
  222. # calculates ratio fo clustered to non-clustered spines along each dendrite
  223. df2_w$ratio <- (df2_w$cluster / df2_w$`no cluster`)
  227. #####
  228. ### 3. example genearlised linear model taking nested data structure into account
  229. # taking df2_w from part 2 of this script
  231. df2_w$ratio <- df2_w$ratio + 0.5 # Gamma distribution does not work on values = 0 -> add a constant to all values
  233. hist(df2_w$ratio) # investigate distribution of dependent variable
  234. stat.desc(df2_w$ratio, basic = F, norm = T) # check whether dependent variable is normally distributed
  235. describeBy(ratio ~ condition + reactivation_status, data = df2_w) # get descriptive statistics for each group
  237. # fixed intercept model
  238. cluster_model0 <- glm(ratio ~ 1, data = df2_w, family = Gamma(link="inverse"))
  239. summary(cluster_model0) #AIC -3
  241. # random intercept model
  242. cluster_model1 <- glmer(ratio ~ 1 + (1 |animal/image), data = df2_w, family = Gamma(link="inverse"))
  243. summary(cluster_model1) #AIC -56
  244. cluster_model2 <- glmer(ratio ~ 1 + (1 |image), data = df2_w, family = Gamma(link="inverse"))
  245. summary(cluster_model2) #AIC -57 -> smallest AIC, use this intercept for predictor model
  246. cluster_model3 <- glmer(ratio ~ 1 + (1 |animal), data = df2_w, family = Gamma(link="inverse"))
  247. summary(cluster_model3) #AIC -32
  249. # compare fit of random vs fixed intercept model
  250. anova(cluster_model2, cluster_model0, test="Chisq") #significant, continue with predictor model
  252. # predictor model
  253. cluster_model4 <- glmer(ratio ~ 1 + condition * reactivation_status + (1 |image), data = df2_w, family = Gamma(link="inverse"))
  254. summary(cluster_model4) # AIC -60
  256. #compare fit of predictor vs random intercept model
  257. anova(cluster_model4, cluster_model2, test="Chisq") #significant, final model can be accepted
  259. emmeans(cluster_model4, pairwise ~ reactivation_status * condition, adjust = "fdr") # post-hoc comparisons
  262. # check assumptions
  263. # normality of residuals
  264. qqPlot(residuals(cluster_model4))
  265. # majority of values falls within prediction, no violation of normality
  267. # homoscedasticity
  268. df2_w$spine_res <- residuals(cluster_model4) # extracts the residuals and places them in a new column in our original data table
  269. df2_w$spine_res <- abs(df2_w$spine_res) # creates a new column with the absolute value of the residuals
  270. df2_w$spine_res2 <- df2_w$spine_res^2 # squares the absolute values of the residuals to provide the more robust estimate
  271. Levene.model <- lm(spine_res2 ~ reactivation_status * condition, data = df2_w) # ANOVA of the squared residuals
  272. anova(Levene.model) # displays the results
  273. # non-significant, no violation of homoscedasticity
  275. # multicollinearity
  276. vif(cluster_model4)
  277. # all values < 5, no violation of multicollinearity
  279. # linearity and homogeneity of residuals
  280. plot(resid(cluster_model4, type = "pearson") ~ fitted(cluster_model4),
  281. xlab = "Fitted values", ylab = "Pearson residuals"
  282. )
  283. abline(h = 0, col = "red")
  284. # small values not perfectly randomly distributed, other variable might lead to this pattern in certain values
  285. # BUT all other assumptions are met
  286. # --> accept final model!

SpineParameter_Analysis+Example.R, under CC-BY-4.0 · at the source

Overview

Authors: Biswajit Moharana1, Panthea Nemat1, Renee M Pullen1, Anna Gradl1, Marijn Schipper2, Jeanne E Savage2, Remco V Klaassen1, Rolinka J van der Loo1, Cora H Chadick3,4, Frank Koopmans1, Yvonne Gouwenberg1, Juan J Garcia Vallejo3, Michel C van den Oever1, August B Smit1, Priyanka Rao-Ruiz1
ORCID iDs: Biswajit Moharana, Panthea Nemat, Renee M Pullen, Anna Gradl, Marijn Schipper, Jeanne E Savage, Remco V Klaassen, Rolinka J van der Loo, Cora H Chadick, Frank Koopmans, Juan J Garcia Vallejo, Michel C van den Oever, August B Smit, Priyanka Rao-Ruiz
  1. Dept. of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
  2. Dept. of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
  3. Microscopy and Cytometry Core Facility, Amsterdam UMC–Location VUMC, Amsterdam, Netherlands
  4. Thermo Fisher Scientific, Greater Seattle Area, Seattle, WA, USA
Institutions: Amsterdam Neuroscience (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Thermo Fisher Scientific (United States) (United States); Amsterdam UMC Location Vrije Universiteit Amsterdam (Netherlands)
Journal: Science advances, volume 12, issue 20, article eadv3557
Dates: received 18 December 2024; accepted 9 April 2026; published online 15 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.adv3557 · PMID 42139347 · PMCID PMC13178557 · OpenAlex W7161267065
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
MeSH: CA1 Region, Hippocampal*, Hippocampus*, Proteome*, Synapses*, Animals, Fear, Memory, Mice, Proteomics (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Dutch Research Council (NWO) (VI.vidi.213.062, 024.004.012)
Citations: not cited yet (Europe PMC); 89 references in the paper

Abstract

Enhanced synaptic wiring onto sparsely distributed memory engram cells supports contextual memory storage and recall, with negative-valence memories exhibiting greater salience. Protein-level adaptations of input-specific synaptic connectivity onto hippocampal CA1 engram cells, however, remain largely unexplored. By combining spatiotemporally restricted synapse labeling, sorting, and mass spectrometry, we generated a discovery-oriented proteomic dataset from samples enriched for CA1 engram cell synapses receiving CA3 input 72 hours after neutral context exploration or aversive contextual fear conditioning. Differential analysis relative to an unlabeled comparator identified protein expression signatures associated with synapse structure, efficacy, and strength in CA1 engram cell synapses. Aversive learning induced predominantly postsynaptic protein expression patterns, whereas neutral context skewed toward presynaptic changes. This differential engram cell synapse proteome was enriched for genes linked to cognitive genetic traits and related disorders. Together, these data provide a hypothesis-generating resource for investigations into the molecular basis of contextual memory at the level of the synapse.

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

Repositories

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Zenodo 18893207

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: DataCite
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), emmeans (1 file), lme4 (1 file), nlme (1 file), pandas (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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At the source: doi.org/10.5281/zenodo.18893207

Zenodo 18893206

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: DataCite
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), emmeans (1 file), lme4 (1 file), nlme (1 file), pandas (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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At the source: doi.org/10.5281/zenodo.18893206

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

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Data

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. All software is listed in table S1. Viral vectors generated by this study will be made available upon reasonable request to the lead contact, P.R.-R. (). The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (88) partner repository with the dataset identifier PXD054892. The raw FCS files and BD Influx analysis files have been deposited to Zenodo under the following accession DOI: https://doi.org/10.5281/zenodo.17486794. Scripts used for synapse structural analysis (34) and MS-DAP analysis (80) have been deposited to Zenodo under the following accession DOI: https://doi.org/10.5281/zenodo.18893207.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 9 MeSH terms, 1 funder, 87 references.

Cite

This paper

Moharana, B., Nemat, P., Pullen, R. M., Gradl, A., Schipper, M., Savage, J. E., Klaassen, R. V., van der Loo, R. J., Chadick, C. H., Koopmans, F., Gouwenberg, Y., Garcia Vallejo, J. J., van den Oever, M. C., Smit, A. B., & Rao-Ruiz, P. (2026). GRASPing experience-dependent protein expression signatures enriched for hippocampal engram cell synapses. Science advances, 12(20), eadv3557. https://doi.org/10.1126/sciadv.adv3557

BibTeX

@article{moharana2026grasping,
author = {Moharana, Biswajit and Nemat, Panthea and Pullen, Renee M and Gradl, Anna and Schipper, Marijn and Savage, Jeanne E and Klaassen, Remco V and van der Loo, Rolinka J and Chadick, Cora H and Koopmans, Frank and Gouwenberg, Yvonne and Garcia Vallejo, Juan J and van den Oever, Michel C and Smit, August B and Rao-Ruiz, Priyanka},
title = {{GRASPing experience-dependent protein expression signatures enriched for hippocampal engram cell synapses}},
journal = {Science advances},
year = {2026},
month = may,
volume = {12},
number = {20},
pages = {eadv3557},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adv3557},
url = {https://doi.org/10.1126/sciadv.adv3557},
pmid = {42139347},
pmcid = {PMC13178557}
}

RIS

TY - JOUR
AU - Moharana, Biswajit
AU - Nemat, Panthea
AU - Pullen, Renee M
AU - Gradl, Anna
AU - Schipper, Marijn
AU - Savage, Jeanne E
AU - Klaassen, Remco V
AU - van der Loo, Rolinka J
AU - Chadick, Cora H
AU - Koopmans, Frank
AU - Gouwenberg, Yvonne
AU - Garcia Vallejo, Juan J
AU - van den Oever, Michel C
AU - Smit, August B
AU - Rao-Ruiz, Priyanka
TI - GRASPing experience-dependent protein expression signatures enriched for hippocampal engram cell synapses
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/05/15
VL - 12
IS - 20
SP - eadv3557
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adv3557
UR - https://doi.org/10.1126/sciadv.adv3557
LA - en
ER -

CSL-JSON

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}
}

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