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Unperturbed dye-based imaging of spontaneous synchronized calcium activity in iPSC-derived neuronal cultures.

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  1. [1] § STAR★Methods › Method details › Analysis of Ca2+ traces ↔ NeuroConnectivity_Func_v01.R, lines 1–39 · score 0.84 · De Vos Lab, NeuroConnectivity, func, modified, intensity, peaks
  2. [2] § STAR★Methods › Method details › Analysis of Ca2+ traces ↔ NeuroConnectivity_Profiling_v01.R, lines 1–36 · score 0.80 · De Vos Lab, NeuroConnectivity, modified, func

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The authors' code

R · 592 lines · 27 KB · CC-BY-NC-SA-4.0 · 1 match

  1. # ------------------------------------------------------------------------------
  2. # NeuroConnectivity - Functional calcium analysis
  3. #
  4. # Author: Winnok H. De Vos
  5. # Modified by: Marlies Verschuuren
  6. # Creation date: 2019-12-13
  7. # Last Modified: 2023-12-20
  8. # ------------------------------------------------------------------------------
  10. #--1. User settings-------------------------------------------------------------
  11. #----1.1. Select directories----------------------------------------------------
  12. # Input: Folder with structure: Rep > Plate > Func > Output
  13. # Rep > Plate > PlateLayout.txt
  14. dir.input="/Users/marliesverschuuren/Documents/UA_DataSets/NeuroConnectivity/PLA/Data"
  15. dir.output="/Users/marliesverschuuren/Library/CloudStorage/OneDrive-UniversiteitAntwerpen/Projects/DeVosLab/NeuroConnectivity/Results_PLA"
  17. #----1.2. Settings analysis------------------------------------------------------
  18. interval = 0.5 # (500 ms)
  19. ctrl.condition="B27_NA_NA" #Condition_Treatment_Concentration
  20. peakheight=1.05 #Height normalised peak (1.05 = 5% increase from median intensity)
  21. peakdistance=5 #Number of frames peak
  22. activePeakNr=5 #Number of peaks to be considered active
  24. #--2. Packages and Settings-----------------------------------------------------
  25. #----2.1. Packages--------------------------------------------------------------
  26. if (!require("tidyverse")) {install.packages("tidyverse"); require("tidyverse")}
  27. if (!require("data.table")) {install.packages("data.table"); require("data.table")}
  28. if (!require("RColorBrewer")) {install.packages("RColorBrewer"); require("RColorBrewer")} #Function: brewer.pal
  29. if (!require("plyr")) {install.packages("plyr"); require("plyr")} #Function: ddply >> Not compatible with dplyr >> Specify dplyr functions
  30. if (!require("gridExtra")) {install.packages("gridExtra"); require("gridExtra")} #Function: marrangegrob
  31. if (!require("pracma")) {install.packages("pracma"); require("pracma")} #Find peaks
  32. if (!require("doParallel")) {install.packages("doParallel"); require("doParallel")} #Parallel computing on multiple cores
  34. #----2.2. Create result directories---------------------------------------------
  35. dir.plot=file.path(dir.output, "Neuro_Func_Plots")
  36. dir.create(dir.plot, showWarnings = FALSE)
  38. dir.mergedData=file.path(dir.output, "Neuro_Func_MergedData")
  39. dir.create(dir.mergedData, showWarnings = FALSE)
  41. #----2.3. Functions-------------------------------------------------------------
  42. # bleach correction (requires index x, intensity y)
  43. bleachcorrect = function(x, y){
  44. params = c()
  45. it = 0
  46. model = NULL
  47. while(TRUE & it<=1)
  48. {
  49. # try exp fit
  50. try(model<-nls(y ~ SSasymp(x, yf, y0, log_alpha)), silent = T)
  51. it = it + 1
  52. }
  53. if(is.null(model))
  54. {
  55. # if exp fit doesn't work, lin fit
  56. model = lm(y ~ x)
  57. p = summary(model)$coefficients[2,4]
  58. }else p=0
  59. if(p<0.05){ # fit needs to have a significant slope coeff
  60. y_bleach = predict(model)
  61. y_corr = y/y_bleach*mean(y,na.rm=T)
  62. }else y_corr=y # no correction applied
  63. }
  65. # normalization functions
  66. norm.fun = function(x) x/median(x, na.rm=T) # divide by the median of the time trace
  68. #--3. Read Func Data ----------------------------------------------------------------
  69. data.roi = data.frame()
  70. folders.rep=list.files(path=file.path(dir.input))
  71. data.list=list()
  72. i=1
  73. #Loop over all rep folders
  74. for (rep in folders.rep){
  75. folders.plate=list.files(path=file.path(dir.input,rep))
  76. #Loop over all Plate folders in Rep folder
  77. for (plate in folders.plate){
  78. data.plate = data.frame()
  79. files = list.files(path = file.path(dir.input,rep,plate,"Func","Output"), pattern = "results", full.names = TRUE)
  81. expr="_[A-Z][0-9]{2}_" #### ADAPT IF NEEDED
  82. #Check regexpr:
  83. #File=files[1]
  84. #str_extract(File,expr)
  86. #Read plate
  87. data.plate = tibble(File = files) %>%
  88. dplyr::mutate(data = lapply(File, fread)) %>%
  89. unnest(data)%>%
  90. dplyr::mutate(Rep=rep,
  91. Plate=plate,
  92. File=File,
  93. Image=substring(str_extract(File,"Output/.+"),8,nchar(str_extract(File,"Output/.+"))-12),
  94. Well=substring(str_extract(File,expr),2,4)) %>% #### ADAPT IF NEEDED
  95. gather(ROI_Image,Mean,-Rep,-Plate,-File,-Image,-Well,-V1)
  97. #Omit missing data
  98. data.plate = na.omit(data.plate)
  100. #Change variable names
  101. data.plate$ROI_Image=as.factor(substring( data.plate$ROI_Image,5,nchar(data.plate$ROI_Image)))
  102. names(data.plate)[which(names(data.plate)=="V1")]="Frame"
  104. #Add image index and roi index within well
  105. data.plate=data.plate%>%
  106. dplyr::group_by(Rep,Plate,Well)%>%
  107. dplyr::mutate(ImageNr = cumsum(!duplicated(Image)),
  108. ROI_Well = cumsum(!duplicated(ROI_Image)))
  110. #Add 0 if well is defined in format B2
  111. data.plate$Well=ifelse(nchar(data.plate$Well)==2,paste(substring(data.plate$Well,1,1),"0",substring(data.plate$Well,2,2),sep=""),data.plate$Well)
  113. #Merge with plate layout
  114. layout=read.table(file.path(dir.input,rep,plate,"PlateLayout.txt"),header=TRUE, sep='\t', fill=TRUE)
  115. data.plate=data.plate%>%
  116. dplyr::left_join(layout)
  118. data.list[[i]]=data.plate
  119. i=i+1
  120. }
  121. }
  123. #Bind all replicates and plates, and assign roi index for whole data set
  124. data.roi=rbindlist(data.list)%>%
  125. as.data.frame()%>%
  126. dplyr::group_by(Rep,Plate,Well,ImageNr,ROI_Image)%>%
  127. dplyr::mutate(ROI_Data = cur_group_id())%>%
  128. dplyr::ungroup()
  130. #Specify time in seconds
  131. data.roi$Time = data.roi$Frame*interval
  133. #Change type variables
  134. data.roi$Rep = as.factor(data.roi$Rep)
  135. data.roi$Plate = as.factor(data.roi$Plate)
  136. data.roi$Well = as.factor(data.roi$Well)
  137. data.roi$ROI_Image = as.factor(data.roi$ROI_Image)
  138. data.roi$ROI_Image=factor(data.roi$ROI_Image, levels = sort(unique(as.numeric(data.roi$ROI_Image))))
  139. data.roi$ROI_Well = as.factor(data.roi$ROI_Well)
  140. data.roi$ROI_Well=factor(data.roi$ROI_Well, levels = sort(unique(as.numeric(data.roi$ROI_Well))))
  141. data.roi$ROI_Data = as.factor(data.roi$ROI_Data)
  142. data.roi$ROI_Data=factor(data.roi$ROI_Data, levels = sort(unique(as.numeric(data.roi$ROI_Data))))
  144. #--4. Plot Raw Data -------------------------------------------------------------
  145. data.temp=data.roi
  146. i=1
  147. plot_list = list()
  148. for(rep in unique(data.temp$Rep)){
  149. data.plot.rep=data.temp[data.temp$Rep==rep,]
  150. for(plate in unique(data.plot.rep$Plate)){
  151. data.plot.plate=data.plot.rep[data.plot.rep$Plate==plate,]
  152. for(well in unique(data.plot.plate$Well)){
  153. data.plot.well=data.plot.plate[data.plot.plate$Well==well,]
  154. for(image in unique(data.plot.well$Image)){
  155. data.plot=data.plot.well[data.plot.well$Image==image,]
  156. p=ggplot(data.plot,aes(Time, Mean, group = as.numeric(ROI_Image), colour = as.numeric(ROI_Image))) +
  157. geom_path() +
  158. scale_colour_viridis_c()+
  159. facet_wrap(ROI_Image~.) +
  160. theme_minimal()+
  161. ggtitle(paste(rep,plate,well,image,sep="_"))+
  162. theme(legend.position = "bottom")
  163. p
  164. plot_list[[i]] = p
  165. i=i+1
  166. }
  167. }
  168. }
  169. }
  170. ggsave(file=file.path(dir.plot,"Func_ROI_Raw.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  172. data.temp=data.roi
  173. i=1
  174. plot_list = list()
  175. for(rep in unique(data.temp$Rep)){
  176. data.plot.rep=data.temp[data.temp$Rep==rep,]
  177. for(plate in unique(data.plot.rep$Plate)){
  178. data.plot.plate=data.plot.rep[data.plot.rep$Plate==plate,]
  179. for(well in unique(data.plot.plate$Well)){
  180. data.plot=data.plot.plate[data.plot.plate$Well==well,]
  181. p=ggplot(data.plot,aes(Time, Mean, group = as.numeric(ROI_Image), colour = as.numeric(ROI_Image))) +
  182. geom_path() +
  183. facet_wrap(Image~.) +
  184. scale_colour_viridis_c()+
  185. theme_minimal()+
  186. ggtitle(paste(rep,plate,well,sep="_"))+
  187. theme(legend.position = "bottom")
  188. p
  189. plot_list[[i]] = p
  190. i=i+1
  191. }
  192. }
  193. }
  194. ggsave(file=file.path(dir.plot,"Func_Image_Raw.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  196. #--5. Bleach correction, normalisation and smoothing per ROI--------------------
  197. data.roi = ddply(data.roi, "ROI_Data", transform, corr.mean = bleachcorrect(Frame, Mean))
  198. data.roi = ddply(data.roi, "ROI_Data", transform, norm.mean = norm.fun(corr.mean))
  199. data.roi = ddply(data.roi, "ROI_Data", transform, smooth.mean = loess(norm.mean ~ Frame, span = 0.02)$fitted) # Very fine smoothing to blunt noise (enhances peak detection) - currently uses 2% points for smoothing
  201. #--6. Plot Normalised data -----------------------------------------------------
  202. data.temp=data.roi
  203. i=1
  204. plot_list = list()
  205. col=c("MeanInt"="black","CorrInt"="dodgerblue","NormInt"="firebrick","SmoothInt"="goldenrod")
  206. for(rep in unique(data.temp$Rep)){
  207. data.plot.rep=data.temp[data.temp$Rep==rep,]
  208. for(plate in unique(data.plot.rep$Plate)){
  209. data.plot.plate=data.plot.rep[data.plot.rep$Plate==plate,]
  210. for(well in unique(data.plot.plate$Well)){
  211. data.plot.well=data.plot.plate[data.plot.plate$Well==well,]
  212. for(image in unique(data.plot.well$Image)){
  213. data.plot=data.plot.well[data.plot.well$Image==image,]
  214. factorAxis=mean(data.plot$Mean)
  215. p=ggplot(data.plot,aes(group = as.numeric(ROI_Image))) +
  216. geom_path(aes(Time, Mean, color = "MeanInt"), linewidth=0.2) +
  217. geom_path(aes(Time, corr.mean, color="CorrInt"),linewidth=0.2) +
  218. geom_path(aes(Time, norm.mean * factorAxis, color="NormInt"),linewidth=0.2) +
  219. geom_path(aes(Time, smooth.mean * factorAxis, color="SmoothInt"),linewidth=0.2) +
  220. scale_color_manual(values = col)+
  221. scale_y_continuous(name = "MeanInt or CorrInt", sec.axis = sec_axis(~./factorAxis, name="NormInt or SmoothInt"))+
  222. facet_wrap(ROI_Image~.) +
  223. labs(color="Legend")+
  224. theme_minimal()+
  225. ggtitle(paste(rep,plate,well,image,sep="_"))+
  226. theme(legend.position = "bottom")
  227. p
  228. plot_list[[i]] = p
  229. i=i+1
  230. }
  231. }
  232. }
  233. }
  234. ggsave(file=file.path(dir.plot,"Func_ROI_Norm.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  236. #--7. Peak detection -----------------------------------------------------------
  237. #----7.1. Peak Detection ROI----------------------------------------------------
  238. data.roi.peak = data.frame()
  239. total.roi.nr = length(unique(data.roi$ROI_Data))
  241. #Define cores for parallel computing
  242. cores=detectCores()
  243. cl = makeCluster(cores[1]-1) #not to overload your computer
  244. registerDoParallel(cl)
  246. #Peak detection
  247. data.roi.peak = foreach(i=1:total.roi.nr, .combine=rbind) %dopar% {
  248. temp = data.roi[which(data.roi$ROI_Data == levels(data.roi$ROI_Data)[i]),]
  249. temp$duration = NA
  250. temp$interval = NA
  251. #findpeaks returns a matrix where each row represents one peak found. The first column gives the height, the second the position/index where the maximum is reached, the third and forth the indices of where the peak begins and ends
  252. peaks = as.data.frame(pracma::findpeaks(temp$smooth.mean, nups = 2, ndowns = 2, minpeakheight = peakheight, minpeakdistance=peakdistance))
  253. if(dim(peaks)[1]!=0){
  254. peaks=peaks[order(peaks$V2),]
  255. }
  256. peaks$duration = (peaks$V4 - peaks$V3) * interval # exact duration of detected wave in sec
  257. temp$peak = ifelse(temp$Frame%in%(peaks$V2),temp$smooth.mean,NA) # labels peak with smooth mean value
  258. temp$peak.start = ifelse(temp$Frame%in%(peaks$V3),temp$smooth.mean,NA) # labels peak with smooth mean value
  259. temp$peak.stop = ifelse(temp$Frame%in%(peaks$V4),temp$smooth.mean,NA) # labels peak with smooth mean value
  260. for (f in 1: dim(peaks)[1])
  261. {
  262. temp$duration[peaks$V4[f]] = peaks$duration[f]
  263. temp$interval[peaks$V2[f]] = ifelse(f==1, peaks$V2[f]* interval, (peaks$V2[f]-peaks$V2[f-1]) * interval )
  264. }
  265. temp #Equivalent to data.roi.peak = rbind(data.roi.peak, temp)
  266. }
  268. #stop cluster
  269. stopCluster(cl)
  271. #----7.2. Peak Detection Average Trace Image------------------------------------
  272. data.image=data.roi%>%
  273. dplyr::group_by(File,Frame,Rep,Plate,Well,Image,ImageNr,Condition,Treatment, Concentration,Time)%>%
  274. dplyr::summarise(norm.mean=mean(norm.mean,na.rm = TRUE),
  275. smooth.mean=mean(smooth.mean,na.rm = TRUE),
  276. Mean=mean(Mean,na.rm = TRUE))%>%
  277. dplyr::ungroup()%>%
  278. dplyr::mutate(Image_ID=paste(Rep,Plate,Well,ImageNr,sep="_")
  279. )
  280. data.image$Image_ID=as.factor(data.image$Image_ID)
  282. data.image.peak = data.frame()
  283. total.roi.nr = length(unique(data.image$Image_ID))
  285. #Define cores for parallel computing
  286. cores=detectCores()
  287. cl = makeCluster(cores[1]-1) #not to overload your computer
  288. registerDoParallel(cl)
  290. #Peak detection
  291. data.image.peak = foreach(i=1:total.roi.nr, .combine=rbind) %dopar% {
  292. temp = data.image[which(data.image$Image_ID == levels(data.image$Image_ID)[i]),]
  293. temp$duration = NA
  294. temp$interval = NA
  295. #findpeaks returns a matrix where each row represents one peak found. The first column gives the height, the second the position/index where the maximum is reached, the third and forth the indices of where the peak begins and ends
  296. peaks = as.data.frame(pracma::findpeaks(temp$smooth.mean, nups = 2, ndowns = 2, minpeakheight = peakheight, minpeakdistance=peakdistance))
  297. if(dim(peaks)[1]!=0){
  298. peaks=peaks[order(peaks$V2),]
  299. }
  300. peaks$duration = (peaks$V4 - peaks$V3) * interval # exact duration of detected wave in sec
  301. temp$peak = ifelse(temp$Frame%in%(peaks$V2),temp$smooth.mean,NA) # labels peak with smooth mean value
  302. temp$peak.start = ifelse(temp$Frame%in%(peaks$V3),temp$smooth.mean,NA) # labels peak with smooth mean value
  303. temp$peak.stop = ifelse(temp$Frame%in%(peaks$V4),temp$smooth.mean,NA) # labels peak with smooth mean value
  304. for (f in 1: dim(peaks)[1])
  305. {
  306. temp$duration[peaks$V4[f]] = peaks$duration[f]
  307. temp$interval[peaks$V2[f]] = ifelse(f==1, peaks$V2[f]* interval, (peaks$V2[f]-peaks$V2[f-1]) * interval )
  308. }
  309. temp #Equivalent to data.roi.peak = rbind(data.roi.peak, temp)
  310. }
  312. #stop cluster
  313. stopCluster(cl)
  315. #----7.3. Plot peaks -----------------------------------------------------------
  316. #Plot peaks per trace
  317. data.temp=data.roi.peak
  318. col=colorRampPalette(c("black","violetred3"))(20)
  319. i=1
  320. for(rep in unique(data.temp$Rep)){
  321. data.plot.rep=data.temp[data.temp$Rep==rep,]
  322. for(plate in unique(data.plot.rep$Plate)){
  323. data.plot.plate=data.plot.rep[data.plot.rep$Plate==plate,]
  324. for(well in unique(data.plot.plate$Well)){
  325. data.plot.well=data.plot.plate[data.plot.plate$Well==well,]
  326. for(image in unique(data.plot.well$Image)){
  327. data.plot=data.plot.well[data.plot.well$Image==image,]
  328. p=ggplot(data.plot) +
  329. geom_path(aes(Time,smooth.mean),color = "black", linewidth=0.5) +
  330. geom_point(aes(Time,peak), color="red", size=0.5)+
  331. geom_point(aes(Time,peak.start), color="orange", size=0.5)+
  332. facet_wrap(~ROI_Image) +
  333. ggtitle(paste(rep,plate,well,image,sep="_"))+
  334. theme_minimal() +
  335. theme(legend.position = "none")
  336. p
  337. plot_list[[i]] = p
  338. i=i+1
  339. }
  340. }
  341. }
  342. }
  343. ggsave(file=file.path(dir.plot,"Func_ROI_Peak.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  345. #Plot peaks on average trace per image
  346. data.temp=data.image.peak
  347. col=colorRampPalette(c("black","violetred3"))(20)
  348. i=1
  349. for(rep in unique(data.temp$Rep)){
  350. data.plot.rep=data.temp[data.temp$Rep==rep,]
  351. for(plate in unique(data.plot.rep$Plate)){
  352. data.plot.plate=data.plot.rep[data.plot.rep$Plate==plate,]
  353. for(well in unique(data.plot.plate$Well)){
  354. data.plot=data.plot.plate[data.plot.plate$Well==well,]
  355. p=ggplot(data.plot) +
  356. geom_path(aes(Time,smooth.mean),color = "black", linewidth=0.5) +
  357. geom_point(aes(Time,peak), color="red", size=0.5)+
  358. geom_point(aes(Time,peak.start), color="orange", size=0.5)+
  359. facet_wrap(.~Image,scales = "free_y") +
  360. ggtitle(paste(rep,plate,well,sep="_"))+
  361. theme_minimal() +
  362. theme(legend.position = "none")
  363. p
  364. plot_list[[i]] = p
  365. i=i+1
  366. }
  367. }
  368. }
  370. ggsave(file=file.path(dir.plot,"Func_Image_AvTrace_Peak.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  372. # Plot peaks per well in a tile plot
  373. data.temp=data.roi.peak
  374. i=1
  375. plot_list = list()
  376. for(rep in unique(data.temp$Rep)){
  377. data.plot.rep=data.temp[data.temp$Rep==rep,]
  378. for(plate in unique(data.plot.rep$Plate)){
  379. data.plot=data.plot.rep[data.plot.rep$Plate==plate,]
  381. p=ggplot(data.plot, aes(Time, as.factor(ROI_Well))) +
  382. geom_tile(data = subset(data.plot, peak>0), fill="black") +
  383. facet_wrap(Well~Image,scales = "free_y") +
  384. ggtitle(paste(rep,plate,sep="_"))+
  385. theme_minimal(base_size = 10) +
  386. theme(panel.grid.major = element_blank(),
  387. panel.grid.minor = element_blank(),
  388. axis.text.y=element_blank(),
  389. legend.position="none")
  390. p
  391. plot_list[[i]] = p
  392. i=i+1
  393. }
  394. }
  395. ggsave(file=file.path(dir.plot,"Func_ROI_Peak_WellTile.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  397. #--8. Activity stats -----------------------------------------------------------
  398. #----8.1. Image Data: Synchronicity between ROIs within an image --------------
  399. cor.data = data.frame()
  400. data.roi.peak$Image_ID=paste(data.roi.peak$Rep,data.roi.peak$Plate,data.roi.peak$Well,data.roi.peak$ImageNr,sep="_")
  401. data.roi.peak$Image_ID=as.factor(data.roi.peak$Image_ID)
  402. image.nr = length(unique(data.roi.peak$Image_ID))
  403. for (i in 1:image.nr){
  404. temp = data.roi.peak[which(data.roi.peak$Image_ID == levels(data.roi.peak$Image_ID)[i]),]
  405. df = as.data.frame(cbind(temp$norm.mean,temp$Time,temp$ROI_Data))
  406. names(df) = c("norm.mean","Time","ROI_Data")
  407. df.d = reshape2::dcast(data = df,formula = Time~ROI_Data, value.var = "norm.mean") # convert dataframe to matrix of ROIs (only retain the norm values per ROI as a function of time)
  408. df.matrix = as.matrix(df.d[, -1]) # remove time
  409. correlations = cor(df.matrix, use="pairwise.complete.obs")
  410. correlations = correlations[upper.tri(correlations, diag=F)] # only retain unique correlations
  411. synchronous.fraction = sum(correlations>0.7)/length(correlations)*100 # consider correlated neurons the ones with more than 70% correlation
  412. av.correlation = mean(correlations,na.rm=T)
  413. cor.data = rbind(cor.data,c(levels(data.roi.peak$Image_ID)[i],synchronous.fraction,av.correlation))
  414. }
  415. names(cor.data) = c("Image_ID","synchronous.fraction","av.correlation")
  416. cor.data$synchronous.fraction=as.numeric(cor.data$synchronous.fraction)
  417. cor.data$av.correlation=as.numeric(cor.data$av.correlation)
  419. #----8.2. Image Data: Global parameters per ROI -------------------------------
  420. features.roi = ddply(data.roi.peak, c("Rep","Plate","Well","Image","ImageNr","Image_ID","Condition","Treatment","Concentration","ROI_Data"), summarise,
  421. baseline.intensity = median(Mean),
  422. peak.nr = length(peak[!is.na(peak)]),
  423. peak.frequency = peak.nr/max(Time),
  424. peak.duration = mean(duration, na.rm=T),
  425. peak.interval = mean(interval, na.rm=T),
  426. peak.duration.variability = sd(duration, na.rm=T)/mean(duration, na.rm=T),
  427. peak.interval.variability = sd(interval, na.rm=T)/mean(interval, na.rm=T),
  428. dynamic.range = max(Mean)- min(Mean),
  429. dynamic.range.norm = max(norm.mean) - min(norm.mean))
  431. features.image.a = ddply(features.roi, c("Rep","Plate","Well","Image","ImageNr","Image_ID","Condition","Treatment","Concentration"), summarise,
  432. baseline.intensity = mean(baseline.intensity),
  433. peak.frequency = mean(peak.frequency, na.rm=T),
  434. peak.duration = mean(peak.duration, na.rm=T),
  435. peak.interval = mean(peak.interval, na.rm=T),
  436. peak.duration.variability = mean(peak.duration.variability, na.rm=T),
  437. peak.interval.variability = mean(peak.interval.variability, na.rm=T),
  438. dynamic.range = mean(dynamic.range),
  439. dynamic.range.norm = mean(dynamic.range.norm))
  441. # summarize roi data per image - with a cutoff for inactive ROIs showing less than 10 peaks across the time window
  442. features.image.b = ddply(features.roi, c("Rep","Plate","Well","Image","ImageNr","Image_ID","Condition","Treatment","Concentration"), summarise,
  443. active.fraction = sum(peak.nr>activePeakNr)/length(peak.nr)*100)
  445. features.image.c = ddply(features.roi[features.roi$peak.nr>activePeakNr,], c("Rep","Plate","Well","Image","ImageNr","Image_ID","Condition","Treatment","Concentration"), summarise,
  446. act.baseline.intensity = mean(baseline.intensity),
  447. act.peak.frequency = mean(peak.frequency),
  448. act.peak.duration = mean(peak.duration),
  449. act.peak.interval = mean(peak.interval),
  450. act.peak.duration.variability = mean(peak.duration.variability),
  451. act.peak.interval.variability = mean(peak.interval.variability),
  452. act.dynamic.range = mean(dynamic.range),
  453. act.dynamic.range.norm = mean(dynamic.range.norm))
  455. features.image.bc=left_join(features.image.b,features.image.c)
  457. #----8.3. Image Data: Global parameters of average trace -------------------------------
  458. features.image.d=ddply(data.image.peak, c("Rep","Plate","Well","Image","ImageNr","Image_ID","Condition","Treatment","Concentration"), summarise,
  459. avTrace.baseline.intensity = median(Mean),
  460. avTrace.peak.nr = length(peak[!is.na(peak)]),
  461. avTrace.peak.frequency = avTrace.peak.nr/max(Time), # peaks per sec
  462. avTrace.dynamic.range = max(Mean) - min(Mean),
  463. avTrace.dynamic.range.norm = max(norm.mean) - min(norm.mean))
  465. features.image.d=subset(features.image.d, select=-c(avTrace.peak.nr))
  467. # combine all image data
  468. data.func.image = left_join(features.image.a,features.image.bc)%>%
  469. dplyr::left_join(features.image.d)%>%
  470. dplyr::left_join(.,cor.data)
  472. data.func.image$ID=paste(data.func.image$Condition,data.func.image$Treatment, data.func.image$Concentration, sep="_")
  474. #----8.4. Well Data: -----------------------------------------------------------
  475. var.features=names(data.func.image)[!names(data.func.image)%in% c("Rep","Plate","Well","Image","ImageNr","Image_ID","Condition","Treatment","Concentration","ID")]
  476. data.func.well=data.func.image%>%
  477. dplyr::group_by(Rep,Plate,Well,Condition,Treatment,Concentration,ID)%>%
  478. dplyr::summarise(across(var.features, mean, na.rm = TRUE))
  480. #----8.5. Normalise to control condition----------------------------------------
  481. unique(data.func.image$ID)
  483. data.func.image.norm = data.func.image%>%
  484. dplyr::group_by(Rep,Plate)%>%
  485. dplyr:: mutate_at(c(var.features),funs(ZScore = (. - mean(.[ID==ctrl.condition], na.rm=TRUE))/sd(.[ID==ctrl.condition],na.rm=TRUE)))%>%
  486. dplyr::select(Rep,Plate,Well,ImageNr,Image_ID,Condition,Treatment,Concentration,ID,contains("ZScore"))
  488. var.features.zscore=paste(var.features,"_ZScore",sep="")
  489. data.func.well.norm=data.func.image.norm%>%
  490. dplyr::group_by(Rep,Plate,Well,Condition,Treatment,Concentration,ID)%>%
  491. dplyr::summarise(across(var.features.zscore, mean, na.rm = TRUE))
  493. #--9. Result plot --------------------------------------------------------------
  494. #----9.1. Well Data ------------------------------------------------------------
  495. data.temp=data.func.well
  496. var.group=c("Rep","Plate","Well","Condition","Treatment","Concentration","ID")
  497. col=brewer.pal(length(unique(data.temp$ID)),"Set3")
  499. i=1
  500. plot_list = list()
  501. for(var in var.features){
  502. data.plot=data.temp[,c(var.group,var)]
  503. names(data.plot)[ncol(data.plot)]="variable"
  504. p=ggplot(data.plot,aes(x = Plate,y = variable, fill=ID, group=ID)) +
  505. stat_summary(fun.data = mean_sdl, geom="errorbar", fun.args = list(mult=1), position=position_dodge(width = 0.9, preserve = "single"), width=.5) +
  506. stat_summary(fun = mean, geom = "bar", position=position_dodge(width = 0.9, preserve = "single"), width=0.85) +
  507. geom_point(position = position_dodge(width = .9), shape=21, fill="grey50", color="white")+
  508. scale_fill_manual(values=col, guide=guide_legend(nrow=2))+
  509. facet_grid(Rep~.)+
  510. ylab(var)+
  511. theme_minimal(base_size = 10)+
  512. theme(legend.position = "bottom")
  513. p
  514. plot_list[[i]] = p
  515. i=i+1
  516. }
  518. ggsave(file=file.path(dir.plot,"Func_Well_Barplot_Rep_Plate.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  520. i=1
  521. plot_list = list()
  522. for(var in var.features){
  523. data.plot=data.temp[,c(var.group,var)]
  524. names(data.plot)[ncol(data.plot)]="variable"
  525. p=ggplot(data.plot,aes(x = NA,y = variable, fill=ID, group=ID)) +
  526. stat_summary(fun.data = mean_sdl, geom="errorbar", fun.args = list(mult=1), position=position_dodge(width = 0.9, preserve = "single"), width=.5) +
  527. stat_summary(fun = mean, geom = "bar", position=position_dodge(width = 0.9, preserve = "single"), width=0.85) +
  528. geom_point(position = position_dodge(width = .9), shape=21, fill="grey50", color="white")+
  529. scale_fill_manual(values=col, guide=guide_legend(nrow=2))+
  530. ylab(var)+
  531. theme_minimal(base_size = 10)+
  532. theme(legend.position = "bottom")
  533. p
  534. plot_list[[i]] = p
  535. i=i+1
  536. }
  538. ggsave(file=file.path(dir.plot,"Func_Well_Barplot.pdf"),marrangeGrob(plot_list, nrow=2, ncol=2),width = 30, height=21, units = "cm")
  540. #----9.2. Well Norm Data -------------------------------------------------------
  541. data.temp=data.func.well.norm
  542. var.group=c("Rep","Plate","Well","Condition","Treatment","Concentration","ID")
  543. col=brewer.pal(length(unique(data.temp$ID)),"Set3")
  545. i=1
  546. plot_list = list()
  547. for(var in var.features.zscore){
  548. data.plot=data.temp[,c(var.group,var)]
  549. names(data.plot)[ncol(data.plot)]="variable"
  550. p=ggplot(data.plot,aes(x = Plate,y = variable, fill=ID, group=ID)) +
  551. stat_summary(fun.data = mean_sdl, geom="errorbar", fun.args = list(mult=1), position=position_dodge(width = 0.9, preserve = "single"), width=.5) +
  552. stat_summary(fun = mean, geom = "bar", position=position_dodge(width = 0.9, preserve = "single"), width=0.85) +
  553. geom_point(position = position_dodge(width = .9), shape=21, fill="grey50", color="white")+
  554. scale_fill_manual(values=col, guide=guide_legend(nrow=2))+
  555. facet_grid(Rep~.)+
  556. ylab(var)+
  557. theme_minimal(base_size = 10)+
  558. theme(legend.position = "bottom")
  559. p
  560. plot_list[[i]] = p
  561. i=i+1
  562. }
  564. ggsave(file=file.path(dir.plot,"Func_Well_Norm_Barplot_Rep_Plate.pdf"),marrangeGrob(plot_list, nrow=1, ncol=1),width = 30, height=21, units = "cm")
  566. i=1
  567. plot_list = list()
  568. for(var in var.features.zscore){
  569. data.plot=data.temp[,c(var.group,var)]
  570. names(data.plot)[ncol(data.plot)]="variable"
  571. p=ggplot(data.plot,aes(x = NA,y = variable, fill=ID, group=ID)) +
  572. stat_summary(fun.data = mean_sdl, geom="errorbar", fun.args = list(mult=1), position=position_dodge(width = 0.9, preserve = "single"), width=.5) +
  573. stat_summary(fun = mean, geom = "bar", position=position_dodge(width = 0.9, preserve = "single"), width=0.85) +
  574. geom_point(position = position_dodge(width = .9), shape=21, fill="grey50", color="white")+
  575. scale_fill_manual(values=col, guide=guide_legend(nrow=2))+
  576. ylab(var)+
  577. theme_minimal(base_size = 10)+
  578. theme(legend.position = "bottom")
  579. p
  580. plot_list[[i]] = p
  581. i=i+1
  582. }
  584. ggsave(file=file.path(dir.plot,"Func_Well_Norm_Barplot.pdf"),marrangeGrob(plot_list, nrow=2, ncol=2),width = 30, height=21, units = "cm")
  586. #--10. Export Data Frames -------------------------------------------------------
  587. write.table(x=data.roi.peak,file=file.path(dir.mergedData,"Data_Func_ROI.txt"), sep='\t', dec=".", col.names = TRUE, row.names = FALSE)
  588. write.table(x=data.func.image,file=file.path(dir.mergedData,"Data_Func_Image.txt"), sep='\t', dec=".", col.names = TRUE, row.names = FALSE)
  589. write.table(x=data.func.well,file=file.path(dir.mergedData,"Data_Func_Well.txt"), sep='\t', dec=".",col.names = TRUE, row.names = FALSE)
  590. write.table(x=data.func.image.norm,file=file.path(dir.mergedData,"Data_Func_Image_Norm.txt"), sep='\t', dec=".", col.names = TRUE, row.names = FALSE)
  591. write.table(x=data.func.well.norm,file=file.path(dir.mergedData,"Data_Func_Well_Norm.txt"), sep='\t', dec=".",col.names = TRUE, row.names = FALSE)

NeuroConnectivity_Func_v01.R at commit 58d821c, under CC-BY-NC-SA-4.0 · at the source

Overview

Authors: Nina Dirkx1,2, Bob Asselbergh1,2, Peter Verstraelen3, Jonas Van Lent2,4, Els De Vriendt1,2, Vincent Timmerman4,5, Winnok H De Vos3,5,6, Sarah Weckhuysen1,5,7,8
ORCID iDs: Nina Dirkx, Vincent Timmerman, Winnok H De Vos, Sarah Weckhuysen
  1. Translational Epilepsy Genomics Group, VIB Center for Molecular Neurology, VIB, 2610 Antwerp, Belgium
  2. Department of Biomedical Sciences, University of Antwerp, 2610 Antwerp, Belgium
  3. Laboratory of Cell Biology and Histology, University of Antwerp, 2610 Antwerp, Belgium
  4. Peripheral Neuropathy Research Group, Department of Biomedical Sciences, University of Antwerp, 2610 Antwerp, Belgium
  5. μNeuro Research Centre of Excellence, University of Antwerp, 2610 Antwerp, Belgium
  6. Antwerp Centre for Advanced Microscopy, University of Antwerp, 2610 Antwerp, Belgium
  7. Division of Neurology, University Hospital Antwerp, 2650 Antwerp, Belgium
  8. Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, 2610 Antwerp, Belgium
Institutions: University of Antwerp (Belgium); VIB-UAntwerp Center for Molecular Neurology (Belgium); Antwerp University Hospital (Belgium)
Journal: iScience, volume 29, issue 5, article 115689
Dates: received 15 September 2025; accepted 8 April 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.115689 · PMID 42080132 · PMCID PMC13133964 · OpenAlex W7152689310
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: imaging methods in chemistry, neuroscience
Topic: Ion channel regulation and function (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Fonds Wetenschappelijk Onderzoek (1861424N, G059325N, G041821N); Queen Elisabeth Medical Foundation for Neurosciences; University of Antwerp
Citations: not cited yet (Europe PMC); 61 references in the paper
Research resources: RRID:CVCL_C9I4, RRID:CVCL_C9I7, RRID:CVCL_C9I9, RRID:CVCL_RC12

Abstract

Synchronous calcium (Ca2+) bursting is a hallmark of neuronal network maturation. While microelectrode array (MEA) recordings are routinely used to generate population-averaged measurements on this functional network activity, live cell Ca2+-imaging offers single-cell resolved, contextual data. Unfortunately, most electrophysiologically active cells are hypersensitive to medium exchange, which is standard practice in most sensor dye-based Ca2+-imaging protocols. Here, we found that the use of conditioned imaging medium preserves spontaneous network activity of iPSC-derived glutamatergic and motor neuron cultures. The effect was consistent across different cell lines and seeding densities and allowed for the faithful detection of disease-specific phenotypes, as shown using a KCNQ2-related epilepsy model. Our findings thus provide a simple, robust strategy to measure spontaneous network activity in Ca2+-imaging experiments, broadening the utility of this technique for functional phenotyping, disease modeling, and drug screening with cellular resolution.

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

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

stardist/stardist

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e80c6de700693bc228ed3c9ba1dc19c3785667ee, 14 February 2026
Languages: Python (40), C/C++ (38), C++ (26), C (15), Jupyter (14)
Size: 201 files, 133 scripts
Software Heritage: archived
Found in: the resources table
Holds: README, license file, environment (pyproject.toml, setup.cfg, setup.py, docker/Dockerfile), tests, continuous integration, 14 notebooks
Not found: CITATION.cff, documentation
Tools: NumPy (19 files), Matplotlib (14 files), tifffile (14 files), scikit-image (7 files), imageio (2 files), Numba (1 file), SciPy (1 file), TensorFlow (1 file), xarray (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
59 files
At the source: github.com/stardist/stardist

DeVosLab/NeuroConnectivity

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 58d821ce9d9b82852e01644da475365390883a40, 15 October 2025
Languages: R (3)
Size: 10 files, 3 scripts
Software Heritage: not archived
Found in: the text, “Analysis of Ca 2+ traces”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (3 files), tidyverse (3 files), pheatmap (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
5 files
At the source: github.com/DeVosLab/NeuroConnectivity

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

Tracing map

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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;
  • 60 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.

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Data

No dataset and no data link were found in the paper.

Data and code availability

Data: All data reported in this paper will be shared by the lead contact upon request.

Code: This paper does not report original code.

Additional information: Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request

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

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 3 funders, 60 references, 4 RRIDs.

Cite

This paper

Dirkx, N., Asselbergh, B., Verstraelen, P., Van Lent, J., De Vriendt, E., Timmerman, V., De Vos, W. H., & Weckhuysen, S. (2026). Unperturbed dye-based imaging of spontaneous synchronized calcium activity in iPSC-derived neuronal cultures. iScience, 29(5), 115689. https://doi.org/10.1016/j.isci.2026.115689

BibTeX

@article{dirkx2026unperturbed,
author = {Dirkx, Nina and Asselbergh, Bob and Verstraelen, Peter and Van Lent, Jonas and De Vriendt, Els and Timmerman, Vincent and De Vos, Winnok H and Weckhuysen, Sarah},
title = {{Unperturbed dye-based imaging of spontaneous synchronized calcium activity in iPSC-derived neuronal cultures}},
journal = {iScience},
year = {2026},
month = apr,
volume = {29},
number = {5},
pages = {115689},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115689},
url = {https://doi.org/10.1016/j.isci.2026.115689},
pmid = {42080132},
pmcid = {PMC13133964}
}

RIS

TY - JOUR
AU - Dirkx, Nina
AU - Asselbergh, Bob
AU - Verstraelen, Peter
AU - Van Lent, Jonas
AU - De Vriendt, Els
AU - Timmerman, Vincent
AU - De Vos, Winnok H
AU - Weckhuysen, Sarah
TI - Unperturbed dye-based imaging of spontaneous synchronized calcium activity in iPSC-derived neuronal cultures
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/04/09
VL - 29
IS - 5
SP - 115689
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115689
UR - https://doi.org/10.1016/j.isci.2026.115689
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.isci.2026.115689",
"type": "article-journal",
"title": "Unperturbed dye-based imaging of spontaneous synchronized calcium activity in iPSC-derived neuronal cultures",
"container-title": "iScience",
"author": [
{
"family": "Dirkx",
"given": "Nina"
},
{
"family": "Asselbergh",
"given": "Bob"
},
{
"family": "Verstraelen",
"given": "Peter"
},
{
"family": "Van Lent",
"given": "Jonas"
},
{
"family": "De Vriendt",
"given": "Els"
},
{
"family": "Timmerman",
"given": "Vincent"
},
{
"family": "De Vos",
"given": "Winnok H"
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{
"family": "Weckhuysen",
"given": "Sarah"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "5",
"page": "115689",
"DOI": "10.1016/j.isci.2026.115689",
"PMID": "42080132",
"PMCID": "PMC13133964",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.115689",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
9
]
]
}
}

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