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Protocol for pH correction of FRET-based fluorescent biosensor imaging in dissociated Drosophila neurons.

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The authors' code

R · 448 lines · 16 KB · no license

  1. # =================================================================
  2. # Laconic Fluorescence Analysis Script
  3. # =================================================================
  4. # This script analyzes fluorescence microscopy data from dual-channel imaging
  5. # It processes both Laconic sensor and pHrodo measurements
  6. # Performs background subtraction, photobleaching correction, and pH corrections
  7. # =================================================================
  9. # INPUT FILE REQUIREMENTS
  10. # =================================================================
  11. # 1. File Naming Convention:
  12. # - Laconic sensor files should be named: [SampleName]_Laconic.csv
  13. # - pHrodo files should be named: [SampleName]_pHrodo.csv
  14. # - Each Laconic file must have a matching pHrodo file
  15. # Example: "Sample1_Laconic.csv" and "Sample1_pHrodo.csv"
  16. #
  17. # 2. Laconic File Format:
  18. # - CSV file with the following columns:
  19. # - Column 1: treatments
  20. # - Column 2: time
  21. # - Columns 3-n: mTFP channel measurements (#1_TFP - #n_TFP)
  22. # - Columns n+2-2n: Venus channel measurements (#n+2_YFP -#2n_YFP)
  23. # - Last column in each channel should be background measurement (n+1 and 2n+1, respectively)
  24. #
  25. # 3. pHrodo File Format:
  26. # - CSV file with the following columns:
  27. # - Column 1: treatments
  28. # - Column 2: time
  29. # - Columns 3-11: TRITC (pHrodo) channel measurements (#1_phrodo - #n_phrodo)
  30. # - Last column should be background measurement
  31. #
  32. # 4. Data Requirements:
  33. # - Both files must have the same number of time points
  34. # - Time points must be aligned between Laconic and pHrodo files
  35. # - Background measurements must be included as the last column
  36. # - No missing values allowed in measurement columns
  37. # =================================================================
  40. # Load required packages
  41. library(tidyverse) # For data manipulation and visualization
  42. library(readr) # For reading CSV files
  43. library(rowr) # For row operations
  44. library(MESS) # For AUC calculations
  45. library(broom) # For converting statistical objects to tidy data frames
  46. library(varhandle) # For variable handling
  47. library(plotrix) # For error calculations
  48. library(aomisc) # For curve fitting
  49. library(CausalImpact) # For time series analysis
  51. # Set working directory and file paths
  52. setwd("~/") # Change this to your working directory
  53. input_path <- "abc" # Directory containing input files
  54. output_path <- "xyz" # Directory for saving results
  56. # Get list of input files
  57. list1 <- as.data.frame(list.files(path=input_path))
  58. colnames(list1) <- c("files")
  60. # Separate pHrodo and Laconic files
  61. list2 <- as.data.frame(list1[c(which(grepl("pHrodo",list1$files)==T)),])
  62. colnames(list2) <- c("files")
  63. list1 <- as.data.frame(list1[-c(which(grepl("pHrodo",list1$files)==T)),])
  64. colnames(list1) <- c("files")
  66. # Process each Laconic file
  67. for (list_files in 1:(nrow(list1))) {
  68. # Read current file
  69. file_uploaded <- as.character(list1[c(list_files),])
  70. ratio_file <- read_csv(paste0(input_path,file_uploaded))
  72. # Remove NA rows if present
  73. rem1 <- which(is.na(ratio_file$time)==T)
  74. if (is_empty(rem1)==F){
  75. ratio_file <- ratio_file[-c(rem1),]
  76. }
  78. # Separate mTFP and Venus channels
  79. col_names <- as.data.frame(colnames(ratio_file))
  80. rem_cols <- which(grepl("TFP",col_names$`colnames(ratio_file)`)==T)
  81. ratio_file_Venus <- as.data.frame(ratio_file[,-c(rem_cols)])
  82. ratio_file_TFP <- as.data.frame(ratio_file[,c(1,2,rem_cols)])
  84. # Background subtraction for Venus channel
  85. for (j in 3:(ncol(ratio_file_Venus)-1)){
  86. col1 <- as.matrix(ratio_file_Venus[,c(j)])
  87. col_back <- as.matrix(ratio_file_Venus[,c(ncol(ratio_file_Venus))])
  88. ratio_file_Venus[,c(j)] <- col1-col_back
  89. }
  90. ratio_file_Venus[,c(ncol(ratio_file_Venus))] <- NULL
  92. # Background subtraction for TFP channel
  93. for (j in 3:(ncol(ratio_file_TFP)-1)){
  94. col1 <- as.matrix(ratio_file_TFP[,c(j)])
  95. col_back <- as.matrix(ratio_file_TFP[,c(ncol(ratio_file_TFP))])
  96. ratio_file_TFP[,c(j)] <- col1-col_back
  97. }
  98. ratio_file_TFP[,c(ncol(ratio_file_TFP))] <- NULL
  100. # Get treatment time point
  101. time_add <- ratio_file[c(which(is.na(ratio_file$treatments)==F)),2]
  102. time_add <- time_add$time
  104. # Get baseline period data
  105. ratio_file_TFP_back <- filter(ratio_file_TFP,ratio_file_TFP$time<time_add[1])
  106. ratio_file_Venus_back <- filter(ratio_file_Venus,ratio_file_Venus$time<time_add[1])
  108. # Photobleaching correction
  109. # Uses exponential decay fitting to correct for fluorescence bleaching
  110. for (i in 3:ncol(ratio_file_TFP)){
  111. # Process TFP channel
  112. full_data <- ratio_file_TFP_back[,c(2,i)]
  113. colnames(full_data) <- c("X","Y")
  115. # Try exponential decay fit
  116. a <- try(nls(Y ~ NLS.expoDecay(X, a, k),data = full_data),silent = TRUE)
  118. if (grepl("Error",a[1])==T){
  119. # If fitting fails, normalize to mean
  120. ratio_file_TFP[,c(i)] <- ratio_file_TFP[,c(i)]/mean(full_data$Y)
  121. } else {
  122. # Apply exponential decay correction
  123. nlsfit <- nls(Y ~ NLS.expoDecay(X, a, k),data = full_data)
  124. nlsfit_vals <- as.data.frame(tidy(nlsfit))
  125. full_data2 <- as.data.frame(ratio_file_TFP[,c(2,i)])
  126. colnames(full_data2) <- c("X","Y")
  127. Y0 <- as.numeric(nlsfit_vals[1,2])
  128. k <- as.numeric(nlsfit_vals[2,2])
  130. # Calculate corrected values
  131. full_data2 <- full_data2 %>%
  132. mutate(Y2=(Y0)*exp((-k)*full_data2$X))
  134. full_data2 <- full_data2 %>%
  135. mutate(Y3=full_data2$Y/full_data2$Y2)
  137. full_data2 <- full_data2 %>%
  138. mutate(Y4=full_data2$Y3*full_data2$Y)
  140. # Apply correction based on trend
  141. if (full_data2[1,3]>full_data2[nrow(full_data2),3]){
  142. ratio_file_TFP[,c(i)] <- full_data2$Y4
  143. } else {
  144. ratio_file_TFP[,c(i)] <- full_data2$Y
  145. }
  146. }
  148. # Repeat process for Venus channel
  149. full_data <- ratio_file_Venus_back[,c(2,i)]
  150. colnames(full_data) <- c("X","Y")
  152. if (grepl("Error",a[1])==T){
  153. ratio_file_Venus[,c(i)] <- ratio_file_Venus[,c(i)]/mean(full_data$Y)
  154. } else {
  155. full_data2 <- as.data.frame(ratio_file_Venus[,c(2,i)])
  156. colnames(full_data2) <- c("X","Y")
  157. full_data2 <- full_data2 %>%
  158. mutate(Y2=(Y0)*exp((-k)*full_data2$X))
  160. full_data2 <- full_data2 %>%
  161. mutate(Y3=full_data2$Y/full_data2$Y2)
  163. full_data2 <- full_data2 %>%
  164. mutate(Y4=full_data2$Y3*full_data2$Y)
  165. ratio_file_Venus[,c(i)] <- full_data2$Y4
  166. }
  167. }
  169. # Calculate TFP/Venus ratios
  170. final_ratios <- ratio_file_Venus
  171. for (k in 3:ncol(final_ratios)){
  172. Venus <- ratio_file_Venus[,c(k)]
  173. TFP <- ratio_file_TFP[,c(k)]
  174. final_ratios[,c(k)] <- TFP/Venus
  175. }
  176. final_ratios_original <- final_ratios
  178. # Normalize to baseline
  179. for (i in 3:ncol(final_ratios)){
  180. col1 <- as.data.frame(final_ratios[,c(i)])
  181. col2 <- col1[c(which(final_ratios$time<time_add[1])),]
  182. norm_val <- mean(col2)
  183. final_ratios[,c(i)] <- final_ratios[,c(i)]/norm_val
  184. }
  187. # Normalization to the pre-mixing baselines using exponential fit
  188. final_ratios_baseline <- final_ratios
  189. final_ratios_corrected <- final_ratios
  190. k_table <- as.data.frame(matrix(0,nrow=(ncol(final_ratios)-2)),ncol=1)
  192. for (i in 3:ncol(final_ratios)){
  193. # Process each trace
  194. full_data <- final_ratios[,c(1,2,i)]
  195. full_data <- filter(full_data,full_data$time<time_add[1])
  196. full_data[,c(1)] <- NULL
  197. colnames(full_data) <- c("X","Y")
  199. # Try different fitting methods
  200. a <- try(nlsfit <- nls(Y ~ NLS.expoDecay(X, a, k),data = full_data),silent = TRUE)
  201. if (grepl("Error",a[1])==T){
  202. b <- try(nls(Y ~ SSasymp(X, yf, y0, log_alpha), data = full_data),silent = TRUE)
  203. if (grepl("Error",b[1])==F){
  204. # Asymptotic regression fit
  205. nlsfit <- nls(Y ~ SSasymp(X, yf, y0, log_alpha), data = full_data)
  206. nlsfit_vals <- as.data.frame(tidy(nlsfit))
  207. full_data2 <- as.data.frame(final_ratios[,c(2,i)])
  208. colnames(full_data2) <- c("X","Y")
  210. # Extract parameters
  211. Y0 <- as.numeric(nlsfit_vals[2,2])
  212. k <- exp(as.numeric(nlsfit_vals[3,2]))
  213. Yf <- as.numeric(nlsfit_vals[1,2])
  215. # Calculate corrections
  216. full_data2 <- full_data2 %>%
  217. mutate(Y2=((Y0-Yf)*exp((-1*k)*full_data2$X))+Yf)
  219. full_data2 <- full_data2 %>%
  220. mutate(Y3=(full_data2$Y/full_data2$Y2)*as.numeric(full_data2[1,2]))
  222. final_ratios_baseline[,c(i)] <- full_data2$Y2
  223. final_ratios_corrected[,c(i)] <- full_data2$Y3
  224. } else {
  225. # If both fits fail
  226. final_ratios_baseline[,c(i)] <- NA
  227. final_ratios_corrected[,c(i)] <- final_ratios[,c(i)]
  228. }
  229. } else {
  230. # Exponential decay fit
  231. nlsfit <- nls(Y ~ NLS.expoDecay(X, a, k),data = full_data)
  232. nlsfit_vals <- as.data.frame(tidy(nlsfit))
  233. full_data2 <- as.data.frame(final_ratios[,c(2,i)])
  234. colnames(full_data2) <- c("X","Y")
  236. # Extract parameters
  237. Y0 <- as.numeric(nlsfit_vals[1,2])
  238. k <- as.numeric(nlsfit_vals[2,2])
  240. # Calculate corrections
  241. full_data2 <- full_data2 %>%
  242. mutate(Y2=(Y0)*exp((-k)*full_data2$X))
  244. full_data2 <- full_data2 %>%
  245. mutate(Y3=(full_data2$Y/full_data2$Y2)*as.numeric(full_data2[1,2]))
  247. final_ratios_baseline[,c(i)] <- full_data2$Y2
  248. final_ratios_corrected[,c(i)] <- full_data2$Y3
  249. k_table[(i-2),] <- k
  250. }
  251. }
  253. # Save baseline correction results
  254. write.csv(final_ratios_corrected,paste0(output_path,"/Laconic_ratios_",file_uploaded))
  256. # Calculate mean and SEM
  257. final_ratios <- final_ratios_corrected
  258. final_ratiosb <- as.data.frame(final_ratios[,c(1,2)])
  259. final_ratiosc <- as.data.frame(final_ratios[,-c(1,2)])
  260. final_ratiosb[,c(3)] <- rowMeans(final_ratiosc)
  262. for (m in 1:nrow(final_ratiosc)){
  263. final_ratiosb[c(m),c(4)] <- std.error(t(final_ratiosc[c(m),]))
  264. }
  266. write.csv(final_ratiosb,paste0(output_path,"/Laconic_ratios_mean_SEM_",file_uploaded))
  269. # Process pHrodo files
  270. # =================================================================
  271. downloaded_file <- as.character(list2[c(list_files),])
  272. pHRed <- read_csv(paste0(input_path, downloaded_file))
  274. # Remove NA rows if present
  275. rem2 <- which(is.na(pHRed$time)==T)
  276. if (is_empty(rem2)==F){
  277. pHRed <- pHRed[-c(rem2),]
  278. }
  280. # Background subtraction for pHrodo data
  281. for (j in 3:(ncol(pHRed)-1)){
  282. col1 <- as.matrix(pHRed[,c(j)])
  283. col1 <- as.numeric(col1)
  284. col_back <- as.matrix(pHRed[,c(ncol(pHRed))])
  285. pHRed[,c(j)] <- col1-col_back
  286. }
  287. pHRed[,c(ncol(pHRed))] <- NULL
  289. # Get baseline period data
  290. pHRed_back <- filter(pHRed, pHRed$time < time_add[1])
  292. # Photobleaching correction for pHrodo
  293. for (i in 3:ncol(pHRed)){
  294. full_data <- pHRed_back[,c(2,i)]
  295. colnames(full_data) <- c("X","Y")
  297. # Try exponential decay fit
  298. a <- try(nls(Y ~ NLS.expoDecay(X, a, k), data = full_data), silent = TRUE)
  300. if (grepl("Error",a[1])==T){
  301. # If fitting fails, normalize to mean
  302. pHRed[,c(i)] <- pHRed[,c(i)]/mean(full_data$Y)
  303. } else {
  304. # Apply exponential decay correction
  305. nlsfit <- nls(Y ~ NLS.expoDecay(X, a, k), data = full_data)
  306. nlsfit_vals <- as.data.frame(tidy(nlsfit))
  307. full_data2 <- as.data.frame(pHRed[,c(2,i)])
  308. colnames(full_data2) <- c("X","Y")
  310. # Extract parameters
  311. Y0 <- as.numeric(nlsfit_vals[1,2])
  312. k <- as.numeric(nlsfit_vals[2,2])
  314. # Calculate corrections
  315. full_data2 <- full_data2 %>%
  316. mutate(Y2=(Y0)*exp((-k)*full_data2$X))
  318. full_data2 <- full_data2 %>%
  319. mutate(Y3=full_data2$Y/full_data2$Y2)
  321. full_data2 <- full_data2 %>%
  322. mutate(Y4=full_data2$Y3*full_data2$Y)
  324. # Apply correction based on trend
  325. if (full_data2[1,3]>full_data2[nrow(full_data2),3]){
  326. pHRed[,c(i)] <- full_data2$Y4
  327. } else {
  328. pHRed[,c(i)] <- full_data2$Y
  329. }
  330. }
  331. }
  333. # Store original and normalized data
  334. pHRed_original <- pHRed
  336. # Normalize to baseline
  337. for (i in 3:ncol(pHRed)){
  338. col1 <- as.data.frame(pHRed[,c(i)])
  339. col2 <- col1[c(which(pHRed$time<time_add[1])),]
  340. norm_val <- mean(col2)
  341. pHRed[,c(i)] <- pHRed[,c(i)]/norm_val
  342. }
  344. # Calculate mean and SEM for normalized data
  345. pHRed_1b <- as.data.frame(pHRed[,c(1,2)])
  346. pHRed_1c <- as.data.frame(pHRed[,-c(1,2)])
  347. pHRed_1b[,c(3)] <- rowMeans(pHRed_1c)
  349. for (m in 1:nrow(pHRed_1c)){
  350. pHRed_1b[c(m),c(4)] <- std.error(t(pHRed_1c[c(m),]))
  351. }
  353. # Save pHrodo results
  354. write.csv(pHRed, paste0(output_path, "/pHRed_", downloaded_file))
  355. write.csv(pHRed_1b, paste0(output_path, "/pHRed_mean_SEM_", downloaded_file))
  357. # pH correction for Laconic sensor data
  358. # =================================================================
  359. final_ratios_corrected <- final_ratios
  361. for (l in 3:ncol(final_ratios)){
  362. # Combine Laconic and pHrodo data
  363. new_col <- as.data.frame(cbind(final_ratios[,c(l)], pHRed[,c(l)]))
  364. colnames(new_col) <- c("laconic","pHRed")
  366. # Separate and normalize baseline and post-treatment periods
  367. new_col2a <- new_col[c(which(final_ratios$time<time_add[1])),]
  368. new_col2b <- new_col[c((which(final_ratios$treatments=="ammonium")-1):nrow(new_col)),]
  369. new_col2b[,1] <- new_col2b[,1]/new_col2b[1,1]
  370. new_col2b[,2] <- new_col2b[,2]/new_col2b[1,2]
  371. new_col2b <- as.data.frame(new_col2b[-c(1),])
  372. new_col3 <- as.data.frame(rbind(new_col2a,new_col2b))
  373. new_col3 <- new_col3-1
  374. write.csv(new_col3,"Desktop/new_col3.csv")
  375. # Linear regression of Laconic vs pH changes
  376. lm_laconic_pH = lm(laconic~pHRed, data = new_col3)
  377. lm_laconic_pH_vals <- as.data.frame(tidy(lm_laconic_pH))
  378. m <- as.numeric(lm_laconic_pH_vals[2,2])
  379. c <- as.numeric(lm_laconic_pH_vals[1,2])
  381. # Apply pH correction
  382. new_col <- new_col-1
  384. new_col <- new_col %>%
  385. mutate(laconic_corrected = m*(new_col$pHRed)+c)
  387. new_col <- new_col %>%
  388. mutate(laconic_corrected2 = 1+(new_col$laconic-new_col$laconic_corrected))
  390. new_col <- new_col %>%
  391. mutate(laconic_corrected2 = (new_col$laconic_corrected2*mean(final_ratios[c(which(final_ratios$time<time_add[1])),l]))/mean(new_col[c(which(final_ratios$time<time_add[1])),4]))
  393. final_ratios_corrected[,c(l)] <- new_col$laconic_corrected2
  394. }
  396. # Calculate mean and SEM for pH-corrected data
  397. final_ratios_correctedb <- as.data.frame(final_ratios_corrected[,c(1,2)])
  398. final_ratios_correctedc <- as.data.frame(final_ratios_corrected[,-c(1,2)])
  399. final_ratios_correctedb[,c(3)] <- rowMeans(final_ratios_correctedc)
  401. for (m in 1:nrow(final_ratios_correctedc)){
  402. final_ratios_correctedb[c(m),c(4)] <- std.error(t(final_ratios_correctedc[c(m),]))
  403. }
  405. # Save pH-corrected results
  406. write.csv(final_ratios_corrected, paste0(output_path, "/laconic_ratios_pH_corrected_", file_uploaded))
  407. write.csv(final_ratios_correctedb, paste0(output_path, "/laconic_ratios_pH_corrected_mean_SEM_", file_uploaded))
  409. # Calculate area under curve for pH-corrected data
  410. area_list1 <- as.data.frame(matrix(0,nrow=0,ncol=1))
  412. for (k in 3:ncol(final_ratios_corrected)){
  413. # Get treatment period data
  414. col1 <- as.data.frame(final_ratios_corrected[c(which(final_ratios_corrected$time>time_add[1] & final_ratios_corrected$time<time_add[2])),c(2,k)])
  415. colnames(col1) <- c("time","ratio")
  417. # Get baseline period data
  418. precol1 <- as.data.frame(final_ratios_corrected[c(final_ratios_corrected$time<time_add[1]),c(2,k)])
  419. colnames(precol1) <- c("time","ratio")
  420. precol2 <- as.data.frame(rbind(precol1,col1))
  422. # Set up periods for causal impact analysis
  423. pre.period <- c(1,nrow(precol1))
  424. post.period <- c((nrow(precol1)+1),nrow(precol2))
  425. precol2 <- as.data.frame(precol2[,order(ncol(precol2):1)])
  427. # Perform causal impact analysis
  428. impact <- CausalImpact(precol2, pre.period, post.period)
  429. impact_vals <- as.data.frame(impact$series)
  430. impact_vals <- as.data.frame(impact_vals[-c(1:nrow(precol1)),])
  432. # Calculate area under curve
  433. col1 <- col1 %>% mutate(base=impact_vals$point.pred)
  434. time1 <- as.numeric(min(col1$time))
  435. time2 <- as.numeric(max(col1$time))
  436. area1 <- auc(col1$time, col1$ratio, from=time1, to=time2)
  437. area2 <- auc(col1$time, col1$base, from=time1, to=time2)
  438. area <- as.data.frame((area1-area2)/nrow(col1))
  440. # Store results
  441. colnames(area) <- c("area")
  442. colnames(area_list1) <- colnames(area)
  443. area_list1 <- as.data.frame(rbind(area_list1, area))
  444. }
  446. # Save pH-corrected AUC results
  447. write.csv(area_list1, paste0(output_path, "/AUC_per_time_pH_corrected_", file_uploaded))
  448. }

Laconic analysis with pH correction.R at commit edf2365, no license · at the source

Overview

Authors: Matthew S Price1,2, Elham Rastegari1, Kartik Venkatachalam1,2,3
ORCID iDs: Kartik Venkatachalam
  1. Department of Integrative Biology and Pharmacology, McGovern Medical School at the University of Texas Health Sciences Center (UTHealth), Houston, TX, USA
  2. Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
  3. Molecular and Translational Biology Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
Institutions: The University of Texas MD Anderson Cancer Center (United States); The University of Texas Health Science Center at Houston (United States)
Journal: STAR protocols, volume 7, issue 3, article 104802
Dates: published online 27 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.xpro.2026.104802 · PMID 42664056 · PMCID PMC13546833 · OpenAlex W7204460820
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), drosophila (organism)
Methods: Statistics, fMRI & imaging
Keywords: Cell Biology, Single Cell, Microscopy, Model Organisms, Neuroscience
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIA NIH HHS (R21 AG087381, R01 AG069076, RF1 AG072176, R01 AG072176, RF1 AG069076)
Citations: not cited yet (Europe PMC); 8 references in the paper
Research resources: w∗;; d42-GAL4 (Drosophila line) RRID:BDSC_8816, R Studio RRID:SCR_000432, R Project for Statistical Computing RRID:SCR_001905, Classint R package RRID:SCR_024515, UTHealth Houston RRID:SCR_025962

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.

Repositories

Its files are read in the Code ↔ Paper reader above.

kvenkatachalam-lab/Price_and_Rastegari_2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: edf236547b9c14d563fa2823a0933861c4bad482, 11 June 2026
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/kvenkatachalam-lab/Price_and_Rastegari_2026

Zenodo 20649762

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source:

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

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;
  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1016/j.xpro.2026.104802.

Versions

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Version 2, 28 September 2026

  • Authors: added Kartik Venkatachalam (0000-0002-3055-9265); removed Kartik Venkatachalam

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 1 funder, 6 references, 5 RRIDs.

Cite

This paper

Price, M. S., Rastegari, E., & Venkatachalam, K. (2026). Protocol for pH correction of FRET-based fluorescent biosensor imaging in dissociated Drosophila neurons. STAR protocols, 7(3), 104802. https://doi.org/10.1016/j.xpro.2026.104802

BibTeX

@article{price2026protocol,
author = {Price, Matthew S and Rastegari, Elham and Venkatachalam, Kartik},
title = {{Protocol for pH correction of FRET-based fluorescent biosensor imaging in dissociated Drosophila neurons}},
journal = {STAR protocols},
year = {2026},
month = aug,
volume = {7},
number = {3},
pages = {104802},
publisher = {Elsevier},
issn = {2666-1667},
doi = {10.1016/j.xpro.2026.104802},
url = {https://doi.org/10.1016/j.xpro.2026.104802},
pmid = {42664056},
pmcid = {PMC13546833}
}

RIS

TY - JOUR
AU - Price, Matthew S
AU - Rastegari, Elham
AU - Venkatachalam, Kartik
TI - Protocol for pH correction of FRET-based fluorescent biosensor imaging in dissociated Drosophila neurons
T2 - STAR protocols
J2 - STAR Protoc
PY - 2026
DA - 2026/08/27
VL - 7
IS - 3
SP - 104802
SN - 2666-1667
PB - Elsevier
DO - 10.1016/j.xpro.2026.104802
UR - https://doi.org/10.1016/j.xpro.2026.104802
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.xpro.2026.104802",
"type": "article-journal",
"title": "Protocol for pH correction of FRET-based fluorescent biosensor imaging in dissociated Drosophila neurons",
"container-title": "STAR protocols",
"author": [
{
"family": "Price",
"given": "Matthew S"
},
{
"family": "Rastegari",
"given": "Elham"
},
{
"family": "Venkatachalam",
"given": "Kartik"
}
],
"container-title-short": "STAR Protoc",
"volume": "7",
"issue": "3",
"page": "104802",
"DOI": "10.1016/j.xpro.2026.104802",
"PMID": "42664056",
"PMCID": "PMC13546833",
"ISSN": "2666-1667",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.xpro.2026.104802",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
27
]
]
}
}

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